id	sid	tid	token	lemma	pos
10_1101-2020_10_26_351783	1	1	A	a	DT
10_1101-2020_10_26_351783	1	2	validated	validate	VBN
10_1101-2020_10_26_351783	1	3	generally	generally	RB
10_1101-2020_10_26_351783	1	4	applicable	applicable	JJ
10_1101-2020_10_26_351783	1	5	approach	approach	NN
10_1101-2020_10_26_351783	1	6	using	use	VBG
10_1101-2020_10_26_351783	1	7	the	the	DT
10_1101-2020_10_26_351783	1	8	systematic	systematic	JJ
10_1101-2020_10_26_351783	1	9	assessment	assessment	NN
10_1101-2020_10_26_351783	1	10	of	of	IN
10_1101-2020_10_26_351783	1	11	disease	disease	NN
10_1101-2020_10_26_351783	1	12	modules	module	NNS
10_1101-2020_10_26_351783	1	13	by	by	IN
10_1101-2020_10_26_351783	1	14	GWAS	GWAS	NNP
10_1101-2020_10_26_351783	1	15	reveals	reveal	VBZ
10_1101-2020_10_26_351783	1	16	a	a	DT
10_1101-2020_10_26_351783	1	17	multi	multi	JJ
10_1101-2020_10_26_351783	1	18	-	-	JJ
10_1101-2020_10_26_351783	1	19	omic	omic	JJ
10_1101-2020_10_26_351783	1	20	module	module	NN
10_1101-2020_10_26_351783	1	21	strongly	strongly	RB
10_1101-2020_10_26_351783	1	22	associated	associate	VBN
10_1101-2020_10_26_351783	1	23	with	with	IN
10_1101-2020_10_26_351783	1	24	risk	risk	NN
10_1101-2020_10_26_351783	1	25	factors	factor	NNS
10_1101-2020_10_26_351783	1	26	in	in	IN
10_1101-2020_10_26_351783	1	27	multiple	multiple	JJ
10_1101-2020_10_26_351783	1	28	sclerosis	sclerosis	NN
10_1101-2020_10_26_351783	1	29	1	1	CD
10_1101-2020_10_26_351783	1	30	A	a	DT
10_1101-2020_10_26_351783	1	31	validated	validate	VBN
10_1101-2020_10_26_351783	1	32	generally	generally	RB
10_1101-2020_10_26_351783	1	33	applicable	applicable	JJ
10_1101-2020_10_26_351783	1	34	approach	approach	NN
10_1101-2020_10_26_351783	1	35	using	use	VBG
10_1101-2020_10_26_351783	1	36	the	the	DT
10_1101-2020_10_26_351783	1	37	systematic	systematic	JJ
10_1101-2020_10_26_351783	1	38	assessment	assessment	NN
10_1101-2020_10_26_351783	1	39	of	of	IN
10_1101-2020_10_26_351783	1	40	disease	disease	NN
10_1101-2020_10_26_351783	1	41	modules	module	NNS
10_1101-2020_10_26_351783	1	42	by	by	IN
10_1101-2020_10_26_351783	1	43	GWAS	GWAS	NNP
10_1101-2020_10_26_351783	1	44	reveals	reveal	VBZ
10_1101-2020_10_26_351783	1	45	a	a	DT
10_1101-2020_10_26_351783	1	46	multi	multi	JJ
10_1101-2020_10_26_351783	1	47	-	-	JJ
10_1101-2020_10_26_351783	1	48	omic	omic	JJ
10_1101-2020_10_26_351783	1	49	module	module	NN
10_1101-2020_10_26_351783	1	50	strongly	strongly	RB
10_1101-2020_10_26_351783	1	51	associated	associate	VBN
10_1101-2020_10_26_351783	1	52	with	with	IN
10_1101-2020_10_26_351783	1	53	risk	risk	NN
10_1101-2020_10_26_351783	1	54	factors	factor	NNS
10_1101-2020_10_26_351783	1	55	in	in	IN
10_1101-2020_10_26_351783	1	56	multiple	multiple	JJ
10_1101-2020_10_26_351783	1	57	sclerosis	sclerosis	NN
10_1101-2020_10_26_351783	1	58	Tejaswi	Tejaswi	NNP
10_1101-2020_10_26_351783	1	59	V.S.	V.S.	NNP
10_1101-2020_10_26_351783	2	1	Badam1,2†	badam1,2†	UH
10_1101-2020_10_26_351783	2	2	,	,	,
10_1101-2020_10_26_351783	2	3	Hendrik	Hendrik	NNP
10_1101-2020_10_26_351783	2	4	A.	A.	NNP
10_1101-2020_10_26_351783	2	5	de	de	NNP
10_1101-2020_10_26_351783	2	6	Weerd1,2†	Weerd1,2†	NNP
10_1101-2020_10_26_351783	2	7	,	,	,
10_1101-2020_10_26_351783	2	8	David	David	NNP
10_1101-2020_10_26_351783	2	9	Martínez	Martínez	NNP
10_1101-2020_10_26_351783	2	10	-	-	HYPH
10_1101-2020_10_26_351783	2	11	Enguita2	Enguita2	NNP
10_1101-2020_10_26_351783	2	12	,	,	,
10_1101-2020_10_26_351783	2	13	Tomas	Tomas	NNP
10_1101-2020_10_26_351783	2	14	Olsson3	Olsson3	NNP
10_1101-2020_10_26_351783	2	15	,	,	,
10_1101-2020_10_26_351783	2	16	Lars	Lars	NNP
10_1101-2020_10_26_351783	2	17	Alfredsson3,4,Ingrid	Alfredsson3,4,Ingrid	NNP
10_1101-2020_10_26_351783	2	18	Kockum3,Maja	Kockum3,Maja	VBZ
10_1101-2020_10_26_351783	2	19	Jagodic3	Jagodic3	NNP
10_1101-2020_10_26_351783	2	20	,	,	,
10_1101-2020_10_26_351783	2	21	Zelmina	Zelmina	NNP
10_1101-2020_10_26_351783	2	22	Lubovac	Lubovac	NNP
10_1101-2020_10_26_351783	2	23	-	-	HYPH
10_1101-2020_10_26_351783	2	24	Pilav1	Pilav1	NNP
10_1101-2020_10_26_351783	2	25	*	*	NFP
10_1101-2020_10_26_351783	2	26	,	,	,
10_1101-2020_10_26_351783	2	27	Mika	Mika	NNP
10_1101-2020_10_26_351783	2	28	Gustafsson2	Gustafsson2	NNP
10_1101-2020_10_26_351783	2	29	*	*	NFP
10_1101-2020_10_26_351783	2	30	1School	1school	CD
10_1101-2020_10_26_351783	2	31	of	of	IN
10_1101-2020_10_26_351783	2	32	Bioscience	Bioscience	NNP
10_1101-2020_10_26_351783	2	33	,	,	,
10_1101-2020_10_26_351783	2	34	Systems	Systems	NNPS
10_1101-2020_10_26_351783	2	35	Biology	Biology	NNP
10_1101-2020_10_26_351783	2	36	Research	Research	NNP
10_1101-2020_10_26_351783	2	37	Center	Center	NNP
10_1101-2020_10_26_351783	2	38	,	,	,
10_1101-2020_10_26_351783	2	39	University	University	NNP
10_1101-2020_10_26_351783	2	40	of	of	IN
10_1101-2020_10_26_351783	2	41	Skövde	Skövde	NNP
10_1101-2020_10_26_351783	2	42	,	,	,
10_1101-2020_10_26_351783	2	43	Sweden	Sweden	NNP
10_1101-2020_10_26_351783	2	44	2Bioinformatics	2Bioinformatics	NNP
10_1101-2020_10_26_351783	2	45	,	,	,
10_1101-2020_10_26_351783	2	46	Department	Department	NNP
10_1101-2020_10_26_351783	2	47	of	of	IN
10_1101-2020_10_26_351783	2	48	Physics	Physics	NNP
10_1101-2020_10_26_351783	2	49	,	,	,
10_1101-2020_10_26_351783	2	50	Chemistry	Chemistry	NNP
10_1101-2020_10_26_351783	2	51	and	and	CC
10_1101-2020_10_26_351783	2	52	Biology	Biology	NNP
10_1101-2020_10_26_351783	2	53	,	,	,
10_1101-2020_10_26_351783	2	54	Linköping	linköpe	VBG
10_1101-2020_10_26_351783	2	55	university	university	NN
10_1101-2020_10_26_351783	2	56	,	,	,
10_1101-2020_10_26_351783	2	57	Linköping	Linköping	NNP
10_1101-2020_10_26_351783	2	58	,	,	,
10_1101-2020_10_26_351783	2	59	Sweden	Sweden	NNP
10_1101-2020_10_26_351783	2	60	3Department	3Department	NNP
10_1101-2020_10_26_351783	2	61	of	of	IN
10_1101-2020_10_26_351783	2	62	Clinical	Clinical	NNP
10_1101-2020_10_26_351783	2	63	Neuroscience	Neuroscience	NNP
10_1101-2020_10_26_351783	2	64	,	,	,
10_1101-2020_10_26_351783	2	65	Karolinska	Karolinska	NNP
10_1101-2020_10_26_351783	2	66	Institutet	Institutet	NNP
10_1101-2020_10_26_351783	2	67	,	,	,
10_1101-2020_10_26_351783	2	68	Center	Center	NNP
10_1101-2020_10_26_351783	2	69	for	for	IN
10_1101-2020_10_26_351783	2	70	Molecular	Molecular	NNP
10_1101-2020_10_26_351783	2	71	Medicine	Medicine	NNP
10_1101-2020_10_26_351783	2	72	,	,	,
10_1101-2020_10_26_351783	2	73	Karolinska	Karolinska	NNP
10_1101-2020_10_26_351783	2	74	University	University	NNP
10_1101-2020_10_26_351783	2	75	Hospital	Hospital	NNP
10_1101-2020_10_26_351783	2	76	,	,	,
10_1101-2020_10_26_351783	2	77	SE-171	SE-171	NNP
10_1101-2020_10_26_351783	2	78	76	76	CD
10_1101-2020_10_26_351783	2	79	,	,	,
10_1101-2020_10_26_351783	2	80	Stockholm	Stockholm	NNP
10_1101-2020_10_26_351783	2	81	,	,	,
10_1101-2020_10_26_351783	2	82	Sweden	Sweden	NNP
10_1101-2020_10_26_351783	2	83	4Institute	4Institute	NNP
10_1101-2020_10_26_351783	2	84	of	of	IN
10_1101-2020_10_26_351783	2	85	Environmental	Environmental	NNP
10_1101-2020_10_26_351783	2	86	Medicine	Medicine	NNP
10_1101-2020_10_26_351783	2	87	,	,	,
10_1101-2020_10_26_351783	2	88	Karolinska	Karolinska	NNP
10_1101-2020_10_26_351783	2	89	Institutet	Institutet	NNP
10_1101-2020_10_26_351783	2	90	,	,	,
10_1101-2020_10_26_351783	2	91	Center	Center	NNP
10_1101-2020_10_26_351783	2	92	for	for	IN
10_1101-2020_10_26_351783	2	93	Molecular	Molecular	NNP
10_1101-2020_10_26_351783	2	94	Medicine	Medicine	NNP
10_1101-2020_10_26_351783	2	95	,	,	,
10_1101-2020_10_26_351783	2	96	Karolinska	Karolinska	NNP
10_1101-2020_10_26_351783	2	97	University	University	NNP
10_1101-2020_10_26_351783	2	98	Hospital	Hospital	NNP
10_1101-2020_10_26_351783	2	99	,	,	,
10_1101-2020_10_26_351783	2	100	SE-171	SE-171	NNP
10_1101-2020_10_26_351783	2	101	76	76	CD
10_1101-2020_10_26_351783	2	102	,	,	,
10_1101-2020_10_26_351783	2	103	Stockholm	Stockholm	NNP
10_1101-2020_10_26_351783	2	104	,	,	,
10_1101-2020_10_26_351783	2	105	Sweden	Sweden	NNP
10_1101-2020_10_26_351783	2	106	†These	†These	NNP
10_1101-2020_10_26_351783	2	107	authors	author	NNS
10_1101-2020_10_26_351783	2	108	contributed	contribute	VBD
10_1101-2020_10_26_351783	2	109	equally	equally	RB
10_1101-2020_10_26_351783	2	110	to	to	IN
10_1101-2020_10_26_351783	2	111	the	the	DT
10_1101-2020_10_26_351783	2	112	work	work	NN
10_1101-2020_10_26_351783	2	113	.	.	.
10_1101-2020_10_26_351783	3	1	*	*	NFP
10_1101-2020_10_26_351783	3	2	These	these	DT
10_1101-2020_10_26_351783	3	3	authors	author	NNS
10_1101-2020_10_26_351783	3	4	share	share	VBP
10_1101-2020_10_26_351783	3	5	senior	senior	JJ
10_1101-2020_10_26_351783	3	6	authorship	authorship	NN
10_1101-2020_10_26_351783	3	7	.	.	.
10_1101-2020_10_26_351783	4	1	Corresponding	correspond	VBG
10_1101-2020_10_26_351783	4	2	author	author	NN
10_1101-2020_10_26_351783	4	3	:	:	:
10_1101-2020_10_26_351783	4	4	Mika	Mika	NNP
10_1101-2020_10_26_351783	4	5	Gustafsson	Gustafsson	NNP
10_1101-2020_10_26_351783	4	6	(	(	-LRB-
10_1101-2020_10_26_351783	4	7	mika.gustafsson@liu.se	mika.gustafsson@liu.se	NNP
10_1101-2020_10_26_351783	4	8	)	)	-RRB-
10_1101-2020_10_26_351783	4	9	Running	run	VBG
10_1101-2020_10_26_351783	4	10	Title	title	NN
10_1101-2020_10_26_351783	4	11	:	:	:
10_1101-2020_10_26_351783	4	12	Multi	multi	JJ
10_1101-2020_10_26_351783	4	13	-	-	JJ
10_1101-2020_10_26_351783	4	14	omic	omic	JJ
10_1101-2020_10_26_351783	4	15	modules	module	NNS
10_1101-2020_10_26_351783	4	16	in	in	IN
10_1101-2020_10_26_351783	4	17	multiple	multiple	JJ
10_1101-2020_10_26_351783	4	18	sclerosis	sclerosis	NN
10_1101-2020_10_26_351783	4	19	Keywords	Keywords	NNPS
10_1101-2020_10_26_351783	4	20	:	:	:
10_1101-2020_10_26_351783	4	21	Benchmark	Benchmark	NNP
10_1101-2020_10_26_351783	4	22	,	,	,
10_1101-2020_10_26_351783	4	23	Multi	Multi	NNP
10_1101-2020_10_26_351783	4	24	-	-	NNS
10_1101-2020_10_26_351783	4	25	omics	omic	NNS
10_1101-2020_10_26_351783	4	26	,	,	,
10_1101-2020_10_26_351783	4	27	Network	Network	NNP
10_1101-2020_10_26_351783	4	28	modules	module	NNS
10_1101-2020_10_26_351783	4	29	,	,	,
10_1101-2020_10_26_351783	4	30	Multiple	Multiple	NNP
10_1101-2020_10_26_351783	4	31	Sclerosis	Sclerosis	NNP
10_1101-2020_10_26_351783	4	32	,	,	,
10_1101-2020_10_26_351783	4	33	Risk	risk	NN
10_1101-2020_10_26_351783	4	34	factors	factor	NNS
10_1101-2020_10_26_351783	4	35	SUMMARY	summary	NN
10_1101-2020_10_26_351783	4	36	:	:	:
10_1101-2020_10_26_351783	4	37	Our	-PRON-	PRP$
10_1101-2020_10_26_351783	4	38	benchmark	benchmark	NN
10_1101-2020_10_26_351783	4	39	of	of	IN
10_1101-2020_10_26_351783	4	40	multi	multi	JJ
10_1101-2020_10_26_351783	4	41	-	-	JJ
10_1101-2020_10_26_351783	4	42	omic	omic	JJ
10_1101-2020_10_26_351783	4	43	modules	module	NNS
10_1101-2020_10_26_351783	4	44	and	and	CC
10_1101-2020_10_26_351783	4	45	validated	validate	VBD
10_1101-2020_10_26_351783	4	46	translational	translational	JJ
10_1101-2020_10_26_351783	4	47	systems	system	NNS
10_1101-2020_10_26_351783	4	48	medicine	medicine	NN
10_1101-2020_10_26_351783	4	49	workflow	workflow	NN
10_1101-2020_10_26_351783	4	50	for	for	IN
10_1101-2020_10_26_351783	4	51	dissecting	dissect	VBG
10_1101-2020_10_26_351783	4	52	complex	complex	JJ
10_1101-2020_10_26_351783	4	53	diseases	disease	NNS
10_1101-2020_10_26_351783	4	54	resulted	result	VBD
10_1101-2020_10_26_351783	4	55	in	in	IN
10_1101-2020_10_26_351783	4	56	multi	multi	JJ
10_1101-2020_10_26_351783	4	57	-	-	JJ
10_1101-2020_10_26_351783	4	58	omic	omic	JJ
10_1101-2020_10_26_351783	4	59	module	module	NN
10_1101-2020_10_26_351783	4	60	of	of	IN
10_1101-2020_10_26_351783	4	61	220	220	CD
10_1101-2020_10_26_351783	4	62	genes	gene	NNS
10_1101-2020_10_26_351783	4	63	highly	highly	RB
10_1101-2020_10_26_351783	4	64	enriched	enrich	VBN
10_1101-2020_10_26_351783	4	65	for	for	IN
10_1101-2020_10_26_351783	4	66	risk	risk	NN
10_1101-2020_10_26_351783	4	67	factors	factor	NNS
10_1101-2020_10_26_351783	4	68	associated	associate	VBN
10_1101-2020_10_26_351783	4	69	with	with	IN
10_1101-2020_10_26_351783	4	70	multiple	multiple	JJ
10_1101-2020_10_26_351783	4	71	sclerosis	sclerosis	NN
10_1101-2020_10_26_351783	4	72	.	.	.
10_1101-2020_10_26_351783	5	1	(	(	-LRB-
10_1101-2020_10_26_351783	5	2	which	which	WDT
10_1101-2020_10_26_351783	5	3	was	be	VBD
10_1101-2020_10_26_351783	5	4	not	not	RB
10_1101-2020_10_26_351783	5	5	certified	certify	VBN
10_1101-2020_10_26_351783	5	6	by	by	IN
10_1101-2020_10_26_351783	5	7	peer	peer	NN
10_1101-2020_10_26_351783	5	8	review	review	NN
10_1101-2020_10_26_351783	5	9	)	)	-RRB-
10_1101-2020_10_26_351783	5	10	is	be	VBZ
10_1101-2020_10_26_351783	5	11	the	the	DT
10_1101-2020_10_26_351783	5	12	author	author	NN
10_1101-2020_10_26_351783	5	13	/	/	SYM
10_1101-2020_10_26_351783	5	14	funder	funder	NN
10_1101-2020_10_26_351783	5	15	.	.	.
10_1101-2020_10_26_351783	6	1	All	all	DT
10_1101-2020_10_26_351783	6	2	rights	right	NNS
10_1101-2020_10_26_351783	6	3	reserved	reserve	VBD
10_1101-2020_10_26_351783	6	4	.	.	.
10_1101-2020_10_26_351783	7	1	No	no	DT
10_1101-2020_10_26_351783	7	2	reuse	reuse	NN
10_1101-2020_10_26_351783	7	3	allowed	allow	VBN
10_1101-2020_10_26_351783	7	4	without	without	IN
10_1101-2020_10_26_351783	7	5	permission	permission	NN
10_1101-2020_10_26_351783	7	6	.	.	.
10_1101-2020_10_26_351783	8	1	The	the	DT
10_1101-2020_10_26_351783	8	2	copyright	copyright	NN
10_1101-2020_10_26_351783	8	3	holder	holder	NN
10_1101-2020_10_26_351783	8	4	for	for	IN
10_1101-2020_10_26_351783	8	5	this	this	DT
10_1101-2020_10_26_351783	8	6	preprintthis	preprintthis	NN
10_1101-2020_10_26_351783	8	7	version	version	NN
10_1101-2020_10_26_351783	8	8	posted	post	VBD
10_1101-2020_10_26_351783	8	9	January	January	NNP
10_1101-2020_10_26_351783	8	10	6	6	CD
10_1101-2020_10_26_351783	8	11	,	,	,
10_1101-2020_10_26_351783	8	12	2021	2021	CD
10_1101-2020_10_26_351783	8	13	.	.	.
10_1101-2020_10_26_351783	8	14	;	;	:
10_1101-2020_10_26_351783	8	15	https://doi.org/10.1101/2020.10.26.351783doi	https://doi.org/10.1101/2020.10.26.351783doi	ADD
10_1101-2020_10_26_351783	8	16	:	:	:
10_1101-2020_10_26_351783	8	17	bioRxiv	biorxiv	VB
10_1101-2020_10_26_351783	8	18	preprint	preprint	NN
10_1101-2020_10_26_351783	8	19	https://doi.org/10.1101/2020.10.26.351783	https://doi.org/10.1101/2020.10.26.351783	CC
10_1101-2020_10_26_351783	8	20	2	2	CD
10_1101-2020_10_26_351783	8	21	ABSTRACT	ABSTRACT	NNP
10_1101-2020_10_26_351783	8	22	Background	background	NN
10_1101-2020_10_26_351783	8	23	:	:	:
10_1101-2020_10_26_351783	8	24	There	there	EX
10_1101-2020_10_26_351783	8	25	are	be	VBP
10_1101-2020_10_26_351783	8	26	few	few	JJ
10_1101-2020_10_26_351783	8	27	(	(	-LRB-
10_1101-2020_10_26_351783	8	28	if	if	IN
10_1101-2020_10_26_351783	8	29	any	any	DT
10_1101-2020_10_26_351783	8	30	)	)	-RRB-
10_1101-2020_10_26_351783	8	31	practical	practical	JJ
10_1101-2020_10_26_351783	8	32	guidelines	guideline	NNS
10_1101-2020_10_26_351783	8	33	for	for	IN
10_1101-2020_10_26_351783	8	34	predictive	predictive	JJ
10_1101-2020_10_26_351783	8	35	and	and	CC
10_1101-2020_10_26_351783	8	36	falsifiable	falsifiable	JJ
10_1101-2020_10_26_351783	8	37	multi	multi	JJ
10_1101-2020_10_26_351783	8	38	-	-	HYPH
10_1101-2020_10_26_351783	8	39	omics	omic	NNS
10_1101-2020_10_26_351783	8	40	data	datum	NNS
10_1101-2020_10_26_351783	8	41	integration	integration	NN
10_1101-2020_10_26_351783	8	42	that	that	WDT
10_1101-2020_10_26_351783	8	43	systematically	systematically	RB
10_1101-2020_10_26_351783	8	44	integrate	integrate	VB
10_1101-2020_10_26_351783	8	45	existing	exist	VBG
10_1101-2020_10_26_351783	8	46	knowledge	knowledge	NN
10_1101-2020_10_26_351783	8	47	.	.	.
10_1101-2020_10_26_351783	9	1	Disease	Disease	NNP
10_1101-2020_10_26_351783	9	2	modules	module	NNS
10_1101-2020_10_26_351783	9	3	are	be	VBP
10_1101-2020_10_26_351783	9	4	popular	popular	JJ
10_1101-2020_10_26_351783	9	5	concepts	concept	NNS
10_1101-2020_10_26_351783	9	6	for	for	IN
10_1101-2020_10_26_351783	9	7	interpreting	interpret	VBG
10_1101-2020_10_26_351783	9	8	genome	genome	NN
10_1101-2020_10_26_351783	9	9	-	-	HYPH
10_1101-2020_10_26_351783	9	10	wide	wide	JJ
10_1101-2020_10_26_351783	9	11	studies	study	NNS
10_1101-2020_10_26_351783	9	12	in	in	IN
10_1101-2020_10_26_351783	9	13	medicine	medicine	NN
10_1101-2020_10_26_351783	9	14	but	but	CC
10_1101-2020_10_26_351783	9	15	have	have	VBP
10_1101-2020_10_26_351783	9	16	so	so	RB
10_1101-2020_10_26_351783	9	17	far	far	RB
10_1101-2020_10_26_351783	9	18	not	not	RB
10_1101-2020_10_26_351783	9	19	been	be	VBN
10_1101-2020_10_26_351783	9	20	systematically	systematically	RB
10_1101-2020_10_26_351783	9	21	evaluated	evaluate	VBN
10_1101-2020_10_26_351783	9	22	and	and	CC
10_1101-2020_10_26_351783	9	23	may	may	MD
10_1101-2020_10_26_351783	9	24	lead	lead	VB
10_1101-2020_10_26_351783	9	25	to	to	IN
10_1101-2020_10_26_351783	9	26	corroborating	corroborate	VBG
10_1101-2020_10_26_351783	9	27	multi	multi	JJ
10_1101-2020_10_26_351783	9	28	-	-	JJ
10_1101-2020_10_26_351783	9	29	omic	omic	JJ
10_1101-2020_10_26_351783	9	30	modules	module	NNS
10_1101-2020_10_26_351783	9	31	.	.	.
10_1101-2020_10_26_351783	10	1	Methods	method	NNS
10_1101-2020_10_26_351783	10	2	:	:	:
10_1101-2020_10_26_351783	10	3	We	-PRON-	PRP
10_1101-2020_10_26_351783	10	4	assessed	assess	VBD
10_1101-2020_10_26_351783	10	5	eight	eight	CD
10_1101-2020_10_26_351783	10	6	module	module	JJ
10_1101-2020_10_26_351783	10	7	identification	identification	NN
10_1101-2020_10_26_351783	10	8	methods	method	NNS
10_1101-2020_10_26_351783	10	9	in	in	IN
10_1101-2020_10_26_351783	10	10	57	57	CD
10_1101-2020_10_26_351783	10	11	previously	previously	RB
10_1101-2020_10_26_351783	10	12	published	publish	VBN
10_1101-2020_10_26_351783	10	13	expression	expression	NN
10_1101-2020_10_26_351783	10	14	and	and	CC
10_1101-2020_10_26_351783	10	15	methylation	methylation	NN
10_1101-2020_10_26_351783	10	16	studies	study	NNS
10_1101-2020_10_26_351783	10	17	of	of	IN
10_1101-2020_10_26_351783	10	18	19	19	CD
10_1101-2020_10_26_351783	10	19	diseases	disease	NNS
10_1101-2020_10_26_351783	10	20	using	use	VBG
10_1101-2020_10_26_351783	10	21	GWAS	GWAS	NNP
10_1101-2020_10_26_351783	10	22	enrichment	enrichment	NN
10_1101-2020_10_26_351783	10	23	analysis	analysis	NN
10_1101-2020_10_26_351783	10	24	.	.	.
10_1101-2020_10_26_351783	11	1	Next	next	RB
10_1101-2020_10_26_351783	11	2	,	,	,
10_1101-2020_10_26_351783	11	3	we	-PRON-	PRP
10_1101-2020_10_26_351783	11	4	applied	apply	VBD
10_1101-2020_10_26_351783	11	5	the	the	DT
10_1101-2020_10_26_351783	11	6	same	same	JJ
10_1101-2020_10_26_351783	11	7	strategy	strategy	NN
10_1101-2020_10_26_351783	11	8	for	for	IN
10_1101-2020_10_26_351783	11	9	multi	multi	JJ
10_1101-2020_10_26_351783	11	10	-	-	NNS
10_1101-2020_10_26_351783	11	11	omics	omic	NNS
10_1101-2020_10_26_351783	11	12	integration	integration	NN
10_1101-2020_10_26_351783	11	13	of	of	IN
10_1101-2020_10_26_351783	11	14	19	19	CD
10_1101-2020_10_26_351783	11	15	datasets	dataset	NNS
10_1101-2020_10_26_351783	11	16	of	of	IN
10_1101-2020_10_26_351783	11	17	multiple	multiple	JJ
10_1101-2020_10_26_351783	11	18	sclerosis	sclerosis	NN
10_1101-2020_10_26_351783	11	19	(	(	-LRB-
10_1101-2020_10_26_351783	11	20	MS	MS	NNP
10_1101-2020_10_26_351783	11	21	)	)	-RRB-
10_1101-2020_10_26_351783	11	22	,	,	,
10_1101-2020_10_26_351783	11	23	and	and	CC
10_1101-2020_10_26_351783	11	24	further	further	RB
10_1101-2020_10_26_351783	11	25	validated	validate	VBD
10_1101-2020_10_26_351783	11	26	the	the	DT
10_1101-2020_10_26_351783	11	27	resulting	result	VBG
10_1101-2020_10_26_351783	11	28	module	module	NN
10_1101-2020_10_26_351783	11	29	using	use	VBG
10_1101-2020_10_26_351783	11	30	both	both	CC
10_1101-2020_10_26_351783	11	31	GWAS	GWAS	NNP
10_1101-2020_10_26_351783	11	32	and	and	CC
10_1101-2020_10_26_351783	11	33	risk	risk	NN
10_1101-2020_10_26_351783	11	34	-	-	HYPH
10_1101-2020_10_26_351783	11	35	factor	factor	NN
10_1101-2020_10_26_351783	11	36	associated	associate	VBN
10_1101-2020_10_26_351783	11	37	genes	gene	NNS
10_1101-2020_10_26_351783	11	38	from	from	IN
10_1101-2020_10_26_351783	11	39	several	several	JJ
10_1101-2020_10_26_351783	11	40	independent	independent	JJ
10_1101-2020_10_26_351783	11	41	cohorts	cohort	NNS
10_1101-2020_10_26_351783	11	42	.	.	.
10_1101-2020_10_26_351783	12	1	Results	result	NNS
10_1101-2020_10_26_351783	12	2	:	:	:
10_1101-2020_10_26_351783	12	3	Our	-PRON-	PRP$
10_1101-2020_10_26_351783	12	4	benchmark	benchmark	NN
10_1101-2020_10_26_351783	12	5	of	of	IN
10_1101-2020_10_26_351783	12	6	modules	module	NNS
10_1101-2020_10_26_351783	12	7	showed	show	VBD
10_1101-2020_10_26_351783	12	8	that	that	IN
10_1101-2020_10_26_351783	12	9	in	in	IN
10_1101-2020_10_26_351783	12	10	immune	immune	NN
10_1101-2020_10_26_351783	12	11	-	-	HYPH
10_1101-2020_10_26_351783	12	12	associated	associate	VBN
10_1101-2020_10_26_351783	12	13	diseases	disease	NNS
10_1101-2020_10_26_351783	12	14	modules	module	NNS
10_1101-2020_10_26_351783	12	15	inferred	infer	VBN
10_1101-2020_10_26_351783	12	16	from	from	IN
10_1101-2020_10_26_351783	12	17	clique	clique	NN
10_1101-2020_10_26_351783	12	18	-	-	HYPH
10_1101-2020_10_26_351783	12	19	based	base	VBN
10_1101-2020_10_26_351783	12	20	methods	method	NNS
10_1101-2020_10_26_351783	12	21	were	be	VBD
10_1101-2020_10_26_351783	12	22	the	the	DT
10_1101-2020_10_26_351783	12	23	most	most	RBS
10_1101-2020_10_26_351783	12	24	enriched	enrich	VBN
10_1101-2020_10_26_351783	12	25	for	for	IN
10_1101-2020_10_26_351783	12	26	GWAS	gwas	NN
10_1101-2020_10_26_351783	12	27	-	-	HYPH
10_1101-2020_10_26_351783	12	28	genes	gene	NNS
10_1101-2020_10_26_351783	12	29	.	.	.
10_1101-2020_10_26_351783	13	1	The	the	DT
10_1101-2020_10_26_351783	13	2	multi	multi	JJ
10_1101-2020_10_26_351783	13	3	-	-	HYPH
10_1101-2020_10_26_351783	13	4	omics	omic	NNS
10_1101-2020_10_26_351783	13	5	case	case	NN
10_1101-2020_10_26_351783	13	6	study	study	NN
10_1101-2020_10_26_351783	13	7	using	use	VBG
10_1101-2020_10_26_351783	13	8	MS	MS	NNP
10_1101-2020_10_26_351783	13	9	revealed	reveal	VBD
10_1101-2020_10_26_351783	13	10	the	the	DT
10_1101-2020_10_26_351783	13	11	robust	robust	JJ
10_1101-2020_10_26_351783	13	12	identification	identification	NN
10_1101-2020_10_26_351783	13	13	of	of	IN
10_1101-2020_10_26_351783	13	14	a	a	DT
10_1101-2020_10_26_351783	13	15	module	module	NN
10_1101-2020_10_26_351783	13	16	of	of	IN
10_1101-2020_10_26_351783	13	17	220	220	CD
10_1101-2020_10_26_351783	13	18	genes	gene	NNS
10_1101-2020_10_26_351783	13	19	.	.	.
10_1101-2020_10_26_351783	14	1	Strikingly	strikingly	RB
10_1101-2020_10_26_351783	14	2	,	,	,
10_1101-2020_10_26_351783	14	3	most	most	JJS
10_1101-2020_10_26_351783	14	4	genes	gene	NNS
10_1101-2020_10_26_351783	14	5	of	of	IN
10_1101-2020_10_26_351783	14	6	the	the	DT
10_1101-2020_10_26_351783	14	7	module	module	NN
10_1101-2020_10_26_351783	14	8	was	be	VBD
10_1101-2020_10_26_351783	14	9	differentially	differentially	RB
10_1101-2020_10_26_351783	14	10	methylated	methylate	VBN
10_1101-2020_10_26_351783	14	11	upon	upon	IN
10_1101-2020_10_26_351783	14	12	the	the	DT
10_1101-2020_10_26_351783	14	13	action	action	NN
10_1101-2020_10_26_351783	14	14	of	of	IN
10_1101-2020_10_26_351783	14	15	one	one	CD
10_1101-2020_10_26_351783	14	16	or	or	CC
10_1101-2020_10_26_351783	14	17	several	several	JJ
10_1101-2020_10_26_351783	14	18	environmental	environmental	JJ
10_1101-2020_10_26_351783	14	19	risk	risk	NN
10_1101-2020_10_26_351783	14	20	factors	factor	NNS
10_1101-2020_10_26_351783	14	21	in	in	IN
10_1101-2020_10_26_351783	14	22	MS	MS	NNP
10_1101-2020_10_26_351783	14	23	(	(	-LRB-
10_1101-2020_10_26_351783	14	24	n	n	NNP
10_1101-2020_10_26_351783	14	25	=	=	SYM
10_1101-2020_10_26_351783	14	26	217	217	CD
10_1101-2020_10_26_351783	14	27	,	,	,
10_1101-2020_10_26_351783	14	28	P	p	NN
10_1101-2020_10_26_351783	14	29	=	=	SYM
10_1101-2020_10_26_351783	14	30	10	10	CD
10_1101-2020_10_26_351783	14	31	-	-	SYM
10_1101-2020_10_26_351783	14	32	47	47	CD
10_1101-2020_10_26_351783	14	33	)	)	-RRB-
10_1101-2020_10_26_351783	14	34	and	and	CC
10_1101-2020_10_26_351783	14	35	were	be	VBD
10_1101-2020_10_26_351783	14	36	also	also	RB
10_1101-2020_10_26_351783	14	37	independently	independently	RB
10_1101-2020_10_26_351783	14	38	validated	validate	VBN
10_1101-2020_10_26_351783	14	39	for	for	IN
10_1101-2020_10_26_351783	14	40	association	association	NN
10_1101-2020_10_26_351783	14	41	with	with	IN
10_1101-2020_10_26_351783	14	42	five	five	CD
10_1101-2020_10_26_351783	14	43	different	different	JJ
10_1101-2020_10_26_351783	14	44	risk	risk	NN
10_1101-2020_10_26_351783	14	45	factors	factor	NNS
10_1101-2020_10_26_351783	14	46	of	of	IN
10_1101-2020_10_26_351783	14	47	MS	MS	NNP
10_1101-2020_10_26_351783	14	48	,	,	,
10_1101-2020_10_26_351783	14	49	which	which	WDT
10_1101-2020_10_26_351783	14	50	further	further	RB
10_1101-2020_10_26_351783	14	51	stressed	stress	VBD
10_1101-2020_10_26_351783	14	52	the	the	DT
10_1101-2020_10_26_351783	14	53	high	high	JJ
10_1101-2020_10_26_351783	14	54	genetic	genetic	JJ
10_1101-2020_10_26_351783	14	55	and	and	CC
10_1101-2020_10_26_351783	14	56	epigenetic	epigenetic	JJ
10_1101-2020_10_26_351783	14	57	relevance	relevance	NN
10_1101-2020_10_26_351783	14	58	of	of	IN
10_1101-2020_10_26_351783	14	59	the	the	DT
10_1101-2020_10_26_351783	14	60	module	module	NN
10_1101-2020_10_26_351783	14	61	for	for	IN
10_1101-2020_10_26_351783	14	62	MS	MS	NNP
10_1101-2020_10_26_351783	14	63	.	.	.
10_1101-2020_10_26_351783	15	1	Conclusion	conclusion	NN
10_1101-2020_10_26_351783	15	2	:	:	:
10_1101-2020_10_26_351783	15	3	We	-PRON-	PRP
10_1101-2020_10_26_351783	15	4	believe	believe	VBP
10_1101-2020_10_26_351783	15	5	our	-PRON-	PRP$
10_1101-2020_10_26_351783	15	6	analysis	analysis	NN
10_1101-2020_10_26_351783	15	7	provides	provide	VBZ
10_1101-2020_10_26_351783	15	8	a	a	DT
10_1101-2020_10_26_351783	15	9	workflow	workflow	NN
10_1101-2020_10_26_351783	15	10	for	for	IN
10_1101-2020_10_26_351783	15	11	selecting	select	VBG
10_1101-2020_10_26_351783	15	12	modules	module	NNS
10_1101-2020_10_26_351783	15	13	and	and	CC
10_1101-2020_10_26_351783	15	14	our	-PRON-	PRP$
10_1101-2020_10_26_351783	15	15	benchmark	benchmark	JJ
10_1101-2020_10_26_351783	15	16	study	study	NN
10_1101-2020_10_26_351783	15	17	may	may	MD
10_1101-2020_10_26_351783	15	18	help	help	VB
10_1101-2020_10_26_351783	15	19	further	further	JJ
10_1101-2020_10_26_351783	15	20	improvement	improvement	NN
10_1101-2020_10_26_351783	15	21	of	of	IN
10_1101-2020_10_26_351783	15	22	disease	disease	NN
10_1101-2020_10_26_351783	15	23	module	module	NN
10_1101-2020_10_26_351783	15	24	methods	method	NNS
10_1101-2020_10_26_351783	15	25	.	.	.
10_1101-2020_10_26_351783	16	1	Moreover	moreover	RB
10_1101-2020_10_26_351783	16	2	,	,	,
10_1101-2020_10_26_351783	16	3	we	-PRON-	PRP
10_1101-2020_10_26_351783	16	4	also	also	RB
10_1101-2020_10_26_351783	16	5	stress	stress	VBP
10_1101-2020_10_26_351783	16	6	that	that	IN
10_1101-2020_10_26_351783	16	7	our	-PRON-	PRP$
10_1101-2020_10_26_351783	16	8	methodology	methodology	NN
10_1101-2020_10_26_351783	16	9	is	be	VBZ
10_1101-2020_10_26_351783	16	10	generally	generally	RB
10_1101-2020_10_26_351783	16	11	applicable	applicable	JJ
10_1101-2020_10_26_351783	16	12	for	for	IN
10_1101-2020_10_26_351783	16	13	combining	combine	VBG
10_1101-2020_10_26_351783	16	14	and	and	CC
10_1101-2020_10_26_351783	16	15	assessing	assess	VBG
10_1101-2020_10_26_351783	16	16	the	the	DT
10_1101-2020_10_26_351783	16	17	performance	performance	NN
10_1101-2020_10_26_351783	16	18	of	of	IN
10_1101-2020_10_26_351783	16	19	multi	multi	JJ
10_1101-2020_10_26_351783	16	20	-	-	JJ
10_1101-2020_10_26_351783	16	21	omics	omics	JJ
10_1101-2020_10_26_351783	16	22	approaches	approach	NNS
10_1101-2020_10_26_351783	16	23	for	for	IN
10_1101-2020_10_26_351783	16	24	complex	complex	JJ
10_1101-2020_10_26_351783	16	25	diseases	disease	NNS
10_1101-2020_10_26_351783	16	26	.	.	.
10_1101-2020_10_26_351783	17	1	(	(	-LRB-
10_1101-2020_10_26_351783	17	2	which	which	WDT
10_1101-2020_10_26_351783	17	3	was	be	VBD
10_1101-2020_10_26_351783	17	4	not	not	RB
10_1101-2020_10_26_351783	17	5	certified	certify	VBN
10_1101-2020_10_26_351783	17	6	by	by	IN
10_1101-2020_10_26_351783	17	7	peer	peer	NN
10_1101-2020_10_26_351783	17	8	review	review	NN
10_1101-2020_10_26_351783	17	9	)	)	-RRB-
10_1101-2020_10_26_351783	17	10	is	be	VBZ
10_1101-2020_10_26_351783	17	11	the	the	DT
10_1101-2020_10_26_351783	17	12	author	author	NN
10_1101-2020_10_26_351783	17	13	/	/	SYM
10_1101-2020_10_26_351783	17	14	funder	funder	NN
10_1101-2020_10_26_351783	17	15	.	.	.
10_1101-2020_10_26_351783	18	1	All	all	DT
10_1101-2020_10_26_351783	18	2	rights	right	NNS
10_1101-2020_10_26_351783	18	3	reserved	reserve	VBD
10_1101-2020_10_26_351783	18	4	.	.	.
10_1101-2020_10_26_351783	19	1	No	no	DT
10_1101-2020_10_26_351783	19	2	reuse	reuse	NN
10_1101-2020_10_26_351783	19	3	allowed	allow	VBN
10_1101-2020_10_26_351783	19	4	without	without	IN
10_1101-2020_10_26_351783	19	5	permission	permission	NN
10_1101-2020_10_26_351783	19	6	.	.	.
10_1101-2020_10_26_351783	20	1	The	the	DT
10_1101-2020_10_26_351783	20	2	copyright	copyright	NN
10_1101-2020_10_26_351783	20	3	holder	holder	NN
10_1101-2020_10_26_351783	20	4	for	for	IN
10_1101-2020_10_26_351783	20	5	this	this	DT
10_1101-2020_10_26_351783	20	6	preprintthis	preprintthis	NN
10_1101-2020_10_26_351783	20	7	version	version	NN
10_1101-2020_10_26_351783	20	8	posted	post	VBD
10_1101-2020_10_26_351783	20	9	January	January	NNP
10_1101-2020_10_26_351783	20	10	6	6	CD
10_1101-2020_10_26_351783	20	11	,	,	,
10_1101-2020_10_26_351783	20	12	2021	2021	CD
10_1101-2020_10_26_351783	20	13	.	.	.
10_1101-2020_10_26_351783	20	14	;	;	:
10_1101-2020_10_26_351783	20	15	https://doi.org/10.1101/2020.10.26.351783doi	https://doi.org/10.1101/2020.10.26.351783doi	ADD
10_1101-2020_10_26_351783	20	16	:	:	:
10_1101-2020_10_26_351783	20	17	bioRxiv	biorxiv	IN
10_1101-2020_10_26_351783	20	18	preprint	preprint	NN
10_1101-2020_10_26_351783	20	19	https://doi.org/10.1101/2020.10.26.351783	https://doi.org/10.1101/2020.10.26.351783	CC
10_1101-2020_10_26_351783	20	20	3	3	CD
10_1101-2020_10_26_351783	20	21	INTRODUCTION	introduction	NN
10_1101-2020_10_26_351783	20	22	Complex	complex	JJ
10_1101-2020_10_26_351783	20	23	diseases	disease	NNS
10_1101-2020_10_26_351783	20	24	are	be	VBP
10_1101-2020_10_26_351783	20	25	the	the	DT
10_1101-2020_10_26_351783	20	26	result	result	NN
10_1101-2020_10_26_351783	20	27	of	of	IN
10_1101-2020_10_26_351783	20	28	disruptions	disruption	NNS
10_1101-2020_10_26_351783	20	29	of	of	IN
10_1101-2020_10_26_351783	20	30	many	many	JJ
10_1101-2020_10_26_351783	20	31	interconnected	interconnected	JJ
10_1101-2020_10_26_351783	20	32	multimolecular	multimolecular	JJ
10_1101-2020_10_26_351783	20	33	pathways	pathway	NNS
10_1101-2020_10_26_351783	20	34	,	,	,
10_1101-2020_10_26_351783	20	35	reflected	reflect	VBN
10_1101-2020_10_26_351783	20	36	in	in	IN
10_1101-2020_10_26_351783	20	37	multiple	multiple	JJ
10_1101-2020_10_26_351783	20	38	omics	omic	NNS
10_1101-2020_10_26_351783	20	39	layers	layer	NNS
10_1101-2020_10_26_351783	20	40	of	of	IN
10_1101-2020_10_26_351783	20	41	regulation	regulation	NN
10_1101-2020_10_26_351783	20	42	of	of	IN
10_1101-2020_10_26_351783	20	43	cellular	cellular	JJ
10_1101-2020_10_26_351783	20	44	function	function	NN
10_1101-2020_10_26_351783	20	45	,	,	,
10_1101-2020_10_26_351783	20	46	rather	rather	RB
10_1101-2020_10_26_351783	20	47	than	than	IN
10_1101-2020_10_26_351783	20	48	perturbations	perturbation	NNS
10_1101-2020_10_26_351783	20	49	of	of	IN
10_1101-2020_10_26_351783	20	50	a	a	DT
10_1101-2020_10_26_351783	20	51	single	single	JJ
10_1101-2020_10_26_351783	20	52	gene	gene	NN
10_1101-2020_10_26_351783	20	53	or	or	CC
10_1101-2020_10_26_351783	20	54	protein[1	protein[1	NNP
10_1101-2020_10_26_351783	20	55	]	]	-RRB-
10_1101-2020_10_26_351783	20	56	.	.	.
10_1101-2020_10_26_351783	21	1	Systems	system	NNS
10_1101-2020_10_26_351783	21	2	and	and	CC
10_1101-2020_10_26_351783	21	3	network	network	NN
10_1101-2020_10_26_351783	21	4	medicine	medicine	NN
10_1101-2020_10_26_351783	21	5	aim	aim	NN
10_1101-2020_10_26_351783	21	6	to	to	TO
10_1101-2020_10_26_351783	21	7	translate	translate	VB
10_1101-2020_10_26_351783	21	8	observed	observed	JJ
10_1101-2020_10_26_351783	21	9	omics	omic	NNS
10_1101-2020_10_26_351783	21	10	differences	difference	NNS
10_1101-2020_10_26_351783	21	11	in	in	IN
10_1101-2020_10_26_351783	21	12	patients	patient	NNS
10_1101-2020_10_26_351783	21	13	using	use	VBG
10_1101-2020_10_26_351783	21	14	networks	network	NNS
10_1101-2020_10_26_351783	21	15	,	,	,
10_1101-2020_10_26_351783	21	16	in	in	IN
10_1101-2020_10_26_351783	21	17	order	order	NN
10_1101-2020_10_26_351783	21	18	to	to	TO
10_1101-2020_10_26_351783	21	19	personalize	personalize	VB
10_1101-2020_10_26_351783	21	20	medicine[2	medicine[2	NNP
10_1101-2020_10_26_351783	21	21	]	]	-RRB-
10_1101-2020_10_26_351783	21	22	.	.	.
10_1101-2020_10_26_351783	22	1	Importantly	importantly	RB
10_1101-2020_10_26_351783	22	2	,	,	,
10_1101-2020_10_26_351783	22	3	genes	gene	NNS
10_1101-2020_10_26_351783	22	4	that	that	WDT
10_1101-2020_10_26_351783	22	5	are	be	VBP
10_1101-2020_10_26_351783	22	6	associated	associate	VBN
10_1101-2020_10_26_351783	22	7	with	with	IN
10_1101-2020_10_26_351783	22	8	diseases	disease	NNS
10_1101-2020_10_26_351783	22	9	are	be	VBP
10_1101-2020_10_26_351783	22	10	more	more	RBR
10_1101-2020_10_26_351783	22	11	likely	likely	JJ
10_1101-2020_10_26_351783	22	12	to	to	TO
10_1101-2020_10_26_351783	22	13	interact	interact	VB
10_1101-2020_10_26_351783	22	14	with	with	IN
10_1101-2020_10_26_351783	22	15	each	each	DT
10_1101-2020_10_26_351783	22	16	other	other	JJ
10_1101-2020_10_26_351783	22	17	rather	rather	RB
10_1101-2020_10_26_351783	22	18	than	than	IN
10_1101-2020_10_26_351783	22	19	with	with	IN
10_1101-2020_10_26_351783	22	20	non	non	JJ
10_1101-2020_10_26_351783	22	21	-	-	JJ
10_1101-2020_10_26_351783	22	22	disease	disease	JJ
10_1101-2020_10_26_351783	22	23	associated	associate	VBN
10_1101-2020_10_26_351783	22	24	genes	gene	NNS
10_1101-2020_10_26_351783	22	25	,	,	,
10_1101-2020_10_26_351783	22	26	forming	form	VBG
10_1101-2020_10_26_351783	22	27	multi	multi	JJ
10_1101-2020_10_26_351783	22	28	-	-	NNS
10_1101-2020_10_26_351783	22	29	omics	omic	NNS
10_1101-2020_10_26_351783	22	30	network	network	NN
10_1101-2020_10_26_351783	22	31	disease	disease	NNP
10_1101-2020_10_26_351783	22	32	modules[3,4	modules[3,4	NNP
10_1101-2020_10_26_351783	22	33	]	]	-RRB-
10_1101-2020_10_26_351783	22	34	.	.	.
10_1101-2020_10_26_351783	23	1	Owing	owe	VBG
10_1101-2020_10_26_351783	23	2	to	to	IN
10_1101-2020_10_26_351783	23	3	the	the	DT
10_1101-2020_10_26_351783	23	4	incompleteness	incompleteness	NN
10_1101-2020_10_26_351783	23	5	of	of	IN
10_1101-2020_10_26_351783	23	6	the	the	DT
10_1101-2020_10_26_351783	23	7	underlying	underlying	JJ
10_1101-2020_10_26_351783	23	8	multi	multi	JJ
10_1101-2020_10_26_351783	23	9	-	-	HYPH
10_1101-2020_10_26_351783	23	10	omics	omic	NNS
10_1101-2020_10_26_351783	23	11	interactions	interaction	NNS
10_1101-2020_10_26_351783	23	12	,	,	,
10_1101-2020_10_26_351783	23	13	the	the	DT
10_1101-2020_10_26_351783	23	14	networks	network	NNS
10_1101-2020_10_26_351783	23	15	are	be	VBP
10_1101-2020_10_26_351783	23	16	often	often	RB
10_1101-2020_10_26_351783	23	17	modeled	model	VBN
10_1101-2020_10_26_351783	23	18	as	as	IN
10_1101-2020_10_26_351783	23	19	effective	effective	JJ
10_1101-2020_10_26_351783	23	20	gene	gene	NN
10_1101-2020_10_26_351783	23	21	-	-	HYPH
10_1101-2020_10_26_351783	23	22	gene	gene	NN
10_1101-2020_10_26_351783	23	23	interactions	interaction	NNS
10_1101-2020_10_26_351783	23	24	,	,	,
10_1101-2020_10_26_351783	23	25	using	use	VBG
10_1101-2020_10_26_351783	23	26	for	for	IN
10_1101-2020_10_26_351783	23	27	example	example	NN
10_1101-2020_10_26_351783	23	28	STRING	STRING	NNP
10_1101-2020_10_26_351783	23	29	database[5	database[5	NNP
10_1101-2020_10_26_351783	23	30	]	]	-RRB-
10_1101-2020_10_26_351783	23	31	.	.	.
10_1101-2020_10_26_351783	24	1	Thus	thus	RB
10_1101-2020_10_26_351783	24	2	,	,	,
10_1101-2020_10_26_351783	24	3	network	network	NN
10_1101-2020_10_26_351783	24	4	modules	module	NNS
10_1101-2020_10_26_351783	24	5	might	may	MD
10_1101-2020_10_26_351783	24	6	be	be	VB
10_1101-2020_10_26_351783	24	7	ideal	ideal	JJ
10_1101-2020_10_26_351783	24	8	tools	tool	NNS
10_1101-2020_10_26_351783	24	9	for	for	IN
10_1101-2020_10_26_351783	24	10	multi	multi	JJ
10_1101-2020_10_26_351783	24	11	-	-	NNS
10_1101-2020_10_26_351783	24	12	omics	omic	NNS
10_1101-2020_10_26_351783	24	13	analysis	analysis	NN
10_1101-2020_10_26_351783	24	14	.	.	.
10_1101-2020_10_26_351783	25	1	However	however	RB
10_1101-2020_10_26_351783	25	2	,	,	,
10_1101-2020_10_26_351783	25	3	the	the	DT
10_1101-2020_10_26_351783	25	4	evaluation	evaluation	NN
10_1101-2020_10_26_351783	25	5	of	of	IN
10_1101-2020_10_26_351783	25	6	performance	performance	NN
10_1101-2020_10_26_351783	25	7	of	of	IN
10_1101-2020_10_26_351783	25	8	different	different	JJ
10_1101-2020_10_26_351783	25	9	module	module	JJ
10_1101-2020_10_26_351783	25	10	inference	inference	NN
10_1101-2020_10_26_351783	25	11	methods	method	NNS
10_1101-2020_10_26_351783	25	12	remains	remain	VBZ
10_1101-2020_10_26_351783	25	13	a	a	DT
10_1101-2020_10_26_351783	25	14	poorly	poorly	RB
10_1101-2020_10_26_351783	25	15	understood	understand	VBN
10_1101-2020_10_26_351783	25	16	topic	topic	NN
10_1101-2020_10_26_351783	25	17	,	,	,
10_1101-2020_10_26_351783	25	18	which	which	WDT
10_1101-2020_10_26_351783	25	19	creates	create	VBZ
10_1101-2020_10_26_351783	25	20	the	the	DT
10_1101-2020_10_26_351783	25	21	need	need	NN
10_1101-2020_10_26_351783	25	22	for	for	IN
10_1101-2020_10_26_351783	25	23	transparent	transparent	JJ
10_1101-2020_10_26_351783	25	24	evaluation	evaluation	NN
10_1101-2020_10_26_351783	25	25	of	of	IN
10_1101-2020_10_26_351783	25	26	these	these	DT
10_1101-2020_10_26_351783	25	27	methods	method	NNS
10_1101-2020_10_26_351783	25	28	based	base	VBN
10_1101-2020_10_26_351783	25	29	on	on	IN
10_1101-2020_10_26_351783	25	30	objective	objective	JJ
10_1101-2020_10_26_351783	25	31	benchmarks	benchmark	NNS
10_1101-2020_10_26_351783	25	32	across	across	IN
10_1101-2020_10_26_351783	25	33	various	various	JJ
10_1101-2020_10_26_351783	25	34	diseases	disease	NNS
10_1101-2020_10_26_351783	25	35	and	and	CC
10_1101-2020_10_26_351783	25	36	omics	omic	NNS
10_1101-2020_10_26_351783	25	37	.	.	.
10_1101-2020_10_26_351783	26	1	Genomic	genomic	JJ
10_1101-2020_10_26_351783	26	2	concordance	concordance	NN
10_1101-2020_10_26_351783	26	3	has	have	VBZ
10_1101-2020_10_26_351783	26	4	been	be	VBN
10_1101-2020_10_26_351783	26	5	suggested	suggest	VBN
10_1101-2020_10_26_351783	26	6	as	as	IN
10_1101-2020_10_26_351783	26	7	a	a	DT
10_1101-2020_10_26_351783	26	8	multi	multi	JJ
10_1101-2020_10_26_351783	26	9	-	-	HYPH
10_1101-2020_10_26_351783	26	10	omics	omic	NNS
10_1101-2020_10_26_351783	26	11	validation	validation	NN
10_1101-2020_10_26_351783	26	12	principle[4,6	principle[4,6	NNP
10_1101-2020_10_26_351783	26	13	]	]	-RRB-
10_1101-2020_10_26_351783	26	14	,	,	,
10_1101-2020_10_26_351783	26	15	i.e.	i.e.	FW
10_1101-2020_10_26_351783	26	16	,	,	,
10_1101-2020_10_26_351783	26	17	modules	module	NNS
10_1101-2020_10_26_351783	26	18	derived	derive	VBN
10_1101-2020_10_26_351783	26	19	from	from	IN
10_1101-2020_10_26_351783	26	20	one	one	CD
10_1101-2020_10_26_351783	26	21	omic	omic	JJ
10_1101-2020_10_26_351783	26	22	,	,	,
10_1101-2020_10_26_351783	26	23	such	such	JJ
10_1101-2020_10_26_351783	26	24	as	as	IN
10_1101-2020_10_26_351783	26	25	gene	gene	NN
10_1101-2020_10_26_351783	26	26	expression	expression	NN
10_1101-2020_10_26_351783	26	27	or	or	CC
10_1101-2020_10_26_351783	26	28	DNA	dna	NN
10_1101-2020_10_26_351783	26	29	methylation	methylation	NN
10_1101-2020_10_26_351783	26	30	should	should	MD
10_1101-2020_10_26_351783	26	31	be	be	VB
10_1101-2020_10_26_351783	26	32	enriched	enrich	VBN
10_1101-2020_10_26_351783	26	33	for	for	IN
10_1101-2020_10_26_351783	26	34	disease-	disease-	NNP
10_1101-2020_10_26_351783	26	35	associated	associated	NNP
10_1101-2020_10_26_351783	26	36	single	single	JJ
10_1101-2020_10_26_351783	26	37	nucleotide	nucleotide	JJ
10_1101-2020_10_26_351783	26	38	polymorphisms	polymorphism	NNS
10_1101-2020_10_26_351783	26	39	(	(	-LRB-
10_1101-2020_10_26_351783	26	40	SNPs	SNPs	NNP
10_1101-2020_10_26_351783	26	41	)	)	-RRB-
10_1101-2020_10_26_351783	26	42	.	.	.
10_1101-2020_10_26_351783	27	1	The	the	DT
10_1101-2020_10_26_351783	27	2	variety	variety	NN
10_1101-2020_10_26_351783	27	3	of	of	IN
10_1101-2020_10_26_351783	27	4	algorithms	algorithm	NNS
10_1101-2020_10_26_351783	27	5	that	that	WDT
10_1101-2020_10_26_351783	27	6	have	have	VBP
10_1101-2020_10_26_351783	27	7	been	be	VBN
10_1101-2020_10_26_351783	27	8	proposed	propose	VBN
10_1101-2020_10_26_351783	27	9	and	and	CC
10_1101-2020_10_26_351783	27	10	applied	apply	VBN
10_1101-2020_10_26_351783	27	11	for	for	IN
10_1101-2020_10_26_351783	27	12	identification	identification	NN
10_1101-2020_10_26_351783	27	13	of	of	IN
10_1101-2020_10_26_351783	27	14	disease	disease	NN
10_1101-2020_10_26_351783	27	15	modules	module	NNS
10_1101-2020_10_26_351783	27	16	can	can	MD
10_1101-2020_10_26_351783	27	17	be	be	VB
10_1101-2020_10_26_351783	27	18	categorized	categorize	VBN
10_1101-2020_10_26_351783	27	19	into	into	IN
10_1101-2020_10_26_351783	27	20	two	two	CD
10_1101-2020_10_26_351783	27	21	main	main	JJ
10_1101-2020_10_26_351783	27	22	groups	group	NNS
10_1101-2020_10_26_351783	27	23	.	.	.
10_1101-2020_10_26_351783	28	1	On	on	IN
10_1101-2020_10_26_351783	28	2	the	the	DT
10_1101-2020_10_26_351783	28	3	one	one	CD
10_1101-2020_10_26_351783	28	4	hand	hand	NN
10_1101-2020_10_26_351783	28	5	,	,	,
10_1101-2020_10_26_351783	28	6	there	there	EX
10_1101-2020_10_26_351783	28	7	are	be	VBP
10_1101-2020_10_26_351783	28	8	methods	method	NNS
10_1101-2020_10_26_351783	28	9	which	which	WDT
10_1101-2020_10_26_351783	28	10	rely	rely	VBP
10_1101-2020_10_26_351783	28	11	purely	purely	RB
10_1101-2020_10_26_351783	28	12	on	on	IN
10_1101-2020_10_26_351783	28	13	clustering	clustering	NN
10_1101-2020_10_26_351783	28	14	of	of	IN
10_1101-2020_10_26_351783	28	15	the	the	DT
10_1101-2020_10_26_351783	28	16	genes	gene	NNS
10_1101-2020_10_26_351783	28	17	in	in	IN
10_1101-2020_10_26_351783	28	18	relevant	relevant	JJ
10_1101-2020_10_26_351783	28	19	disease	disease	NN
10_1101-2020_10_26_351783	28	20	networks[7	networks[7	NNP
10_1101-2020_10_26_351783	28	21	]	]	-RRB-
10_1101-2020_10_26_351783	28	22	.	.	.
10_1101-2020_10_26_351783	29	1	On	on	IN
10_1101-2020_10_26_351783	29	2	the	the	DT
10_1101-2020_10_26_351783	29	3	other	other	JJ
10_1101-2020_10_26_351783	29	4	hand	hand	NN
10_1101-2020_10_26_351783	29	5	,	,	,
10_1101-2020_10_26_351783	29	6	there	there	EX
10_1101-2020_10_26_351783	29	7	are	be	VBP
10_1101-2020_10_26_351783	29	8	algorithms	algorithm	NNS
10_1101-2020_10_26_351783	29	9	which	which	WDT
10_1101-2020_10_26_351783	29	10	make	make	VBP
10_1101-2020_10_26_351783	29	11	use	use	NN
10_1101-2020_10_26_351783	29	12	of	of	IN
10_1101-2020_10_26_351783	29	13	disease	disease	NN
10_1101-2020_10_26_351783	29	14	-	-	HYPH
10_1101-2020_10_26_351783	29	15	associated	associate	VBN
10_1101-2020_10_26_351783	29	16	molecules	molecule	NNS
10_1101-2020_10_26_351783	29	17	or	or	CC
10_1101-2020_10_26_351783	29	18	genetic	genetic	JJ
10_1101-2020_10_26_351783	29	19	loci	loci	NN
10_1101-2020_10_26_351783	29	20	to	to	TO
10_1101-2020_10_26_351783	29	21	reveal	reveal	VB
10_1101-2020_10_26_351783	29	22	disease	disease	NN
10_1101-2020_10_26_351783	29	23	modules	module	NNS
10_1101-2020_10_26_351783	29	24	that	that	WDT
10_1101-2020_10_26_351783	29	25	correlate	correlate	VBP
10_1101-2020_10_26_351783	29	26	with	with	IN
10_1101-2020_10_26_351783	29	27	disease	disease	NN
10_1101-2020_10_26_351783	29	28	function	function	NN
10_1101-2020_10_26_351783	29	29	,	,	,
10_1101-2020_10_26_351783	29	30	such	such	JJ
10_1101-2020_10_26_351783	29	31	as	as	IN
10_1101-2020_10_26_351783	29	32	the	the	DT
10_1101-2020_10_26_351783	29	33	disease	disease	NN
10_1101-2020_10_26_351783	29	34	module	module	NNP
10_1101-2020_10_26_351783	29	35	detection	detection	NN
10_1101-2020_10_26_351783	29	36	(	(	-LRB-
10_1101-2020_10_26_351783	29	37	DIAMOnD	diamond	NN
10_1101-2020_10_26_351783	29	38	)	)	-RRB-
10_1101-2020_10_26_351783	29	39	algorithm[8	algorithm[8	NNP
10_1101-2020_10_26_351783	29	40	]	]	-RRB-
10_1101-2020_10_26_351783	29	41	,	,	,
10_1101-2020_10_26_351783	29	42	clique	clique	NN
10_1101-2020_10_26_351783	29	43	-	-	HYPH
10_1101-2020_10_26_351783	29	44	based	base	VBN
10_1101-2020_10_26_351783	29	45	methods[9],[10	methods[9],[10	NNP
10_1101-2020_10_26_351783	29	46	]	]	-RRB-
10_1101-2020_10_26_351783	29	47	and	and	CC
10_1101-2020_10_26_351783	29	48	weighted	weight	VBD
10_1101-2020_10_26_351783	29	49	gene	gene	NN
10_1101-2020_10_26_351783	29	50	co	co	NN
10_1101-2020_10_26_351783	29	51	-	-	JJ
10_1101-2020_10_26_351783	29	52	expression	expression	JJ
10_1101-2020_10_26_351783	29	53	network	network	NN
10_1101-2020_10_26_351783	29	54	analysis	analysis	NN
10_1101-2020_10_26_351783	29	55	(	(	-LRB-
10_1101-2020_10_26_351783	29	56	WGCNA)[11	WGCNA)[11	NNP
10_1101-2020_10_26_351783	29	57	]	]	-RRB-
10_1101-2020_10_26_351783	29	58	.	.	.
10_1101-2020_10_26_351783	30	1	The	the	DT
10_1101-2020_10_26_351783	30	2	data	data	NN
10_1101-2020_10_26_351783	30	3	-	-	HYPH
10_1101-2020_10_26_351783	30	4	derived	derive	VBN
10_1101-2020_10_26_351783	30	5	information	information	NN
10_1101-2020_10_26_351783	30	6	can	can	MD
10_1101-2020_10_26_351783	30	7	either	either	CC
10_1101-2020_10_26_351783	30	8	be	be	VB
10_1101-2020_10_26_351783	30	9	differentially	differentially	RB
10_1101-2020_10_26_351783	30	10	expressed	express	VBN
10_1101-2020_10_26_351783	30	11	genes	gene	NNS
10_1101-2020_10_26_351783	30	12	or	or	CC
10_1101-2020_10_26_351783	30	13	differentially	differentially	RB
10_1101-2020_10_26_351783	30	14	correlated	correlate	VBN
10_1101-2020_10_26_351783	30	15	or	or	CC
10_1101-2020_10_26_351783	30	16	co	co	VBN
10_1101-2020_10_26_351783	30	17	-	-	VBN
10_1101-2020_10_26_351783	30	18	expressed	expressed	JJ
10_1101-2020_10_26_351783	30	19	genes	gene	NNS
10_1101-2020_10_26_351783	30	20	.	.	.
10_1101-2020_10_26_351783	31	1	Methods	method	NNS
10_1101-2020_10_26_351783	31	2	following	follow	VBG
10_1101-2020_10_26_351783	31	3	the	the	DT
10_1101-2020_10_26_351783	31	4	former	former	JJ
10_1101-2020_10_26_351783	31	5	approach	approach	NN
10_1101-2020_10_26_351783	31	6	were	be	VBD
10_1101-2020_10_26_351783	31	7	recently	recently	RB
10_1101-2020_10_26_351783	31	8	benchmarked	benchmarke	VBN
10_1101-2020_10_26_351783	31	9	by	by	IN
10_1101-2020_10_26_351783	31	10	a	a	DT
10_1101-2020_10_26_351783	31	11	metric	metric	JJ
10_1101-2020_10_26_351783	31	12	utilizing	utilize	VBG
10_1101-2020_10_26_351783	31	13	genomic	genomic	JJ
10_1101-2020_10_26_351783	31	14	concordance	concordance	NN
10_1101-2020_10_26_351783	31	15	within	within	IN
10_1101-2020_10_26_351783	31	16	the	the	DT
10_1101-2020_10_26_351783	31	17	DREAM	dream	NN
10_1101-2020_10_26_351783	31	18	consortia[12	consortia[12	NN
10_1101-2020_10_26_351783	31	19	]	]	-RRB-
10_1101-2020_10_26_351783	31	20	.	.	.
10_1101-2020_10_26_351783	32	1	However	however	RB
10_1101-2020_10_26_351783	32	2	,	,	,
10_1101-2020_10_26_351783	32	3	so	so	RB
10_1101-2020_10_26_351783	32	4	far	far	RB
10_1101-2020_10_26_351783	32	5	,	,	,
10_1101-2020_10_26_351783	32	6	algorithms	algorithm	NNS
10_1101-2020_10_26_351783	32	7	from	from	IN
10_1101-2020_10_26_351783	32	8	the	the	DT
10_1101-2020_10_26_351783	32	9	latter	latter	JJ
10_1101-2020_10_26_351783	32	10	group	group	NN
10_1101-2020_10_26_351783	32	11	have	have	VBP
10_1101-2020_10_26_351783	32	12	not	not	RB
10_1101-2020_10_26_351783	32	13	been	be	VBN
10_1101-2020_10_26_351783	32	14	benchmarked	benchmarke	VBN
10_1101-2020_10_26_351783	32	15	.	.	.
10_1101-2020_10_26_351783	33	1	(	(	-LRB-
10_1101-2020_10_26_351783	33	2	which	which	WDT
10_1101-2020_10_26_351783	33	3	was	be	VBD
10_1101-2020_10_26_351783	33	4	not	not	RB
10_1101-2020_10_26_351783	33	5	certified	certify	VBN
10_1101-2020_10_26_351783	33	6	by	by	IN
10_1101-2020_10_26_351783	33	7	peer	peer	NN
10_1101-2020_10_26_351783	33	8	review	review	NN
10_1101-2020_10_26_351783	33	9	)	)	-RRB-
10_1101-2020_10_26_351783	33	10	is	be	VBZ
10_1101-2020_10_26_351783	33	11	the	the	DT
10_1101-2020_10_26_351783	33	12	author	author	NN
10_1101-2020_10_26_351783	33	13	/	/	SYM
10_1101-2020_10_26_351783	33	14	funder	funder	NN
10_1101-2020_10_26_351783	33	15	.	.	.
10_1101-2020_10_26_351783	34	1	All	all	DT
10_1101-2020_10_26_351783	34	2	rights	right	NNS
10_1101-2020_10_26_351783	34	3	reserved	reserve	VBD
10_1101-2020_10_26_351783	34	4	.	.	.
10_1101-2020_10_26_351783	35	1	No	no	DT
10_1101-2020_10_26_351783	35	2	reuse	reuse	NN
10_1101-2020_10_26_351783	35	3	allowed	allow	VBN
10_1101-2020_10_26_351783	35	4	without	without	IN
10_1101-2020_10_26_351783	35	5	permission	permission	NN
10_1101-2020_10_26_351783	35	6	.	.	.
10_1101-2020_10_26_351783	36	1	The	the	DT
10_1101-2020_10_26_351783	36	2	copyright	copyright	NN
10_1101-2020_10_26_351783	36	3	holder	holder	NN
10_1101-2020_10_26_351783	36	4	for	for	IN
10_1101-2020_10_26_351783	36	5	this	this	DT
10_1101-2020_10_26_351783	36	6	preprintthis	preprintthis	NN
10_1101-2020_10_26_351783	36	7	version	version	NN
10_1101-2020_10_26_351783	36	8	posted	post	VBD
10_1101-2020_10_26_351783	36	9	January	January	NNP
10_1101-2020_10_26_351783	36	10	6	6	CD
10_1101-2020_10_26_351783	36	11	,	,	,
10_1101-2020_10_26_351783	36	12	2021	2021	CD
10_1101-2020_10_26_351783	36	13	.	.	.
10_1101-2020_10_26_351783	36	14	;	;	:
10_1101-2020_10_26_351783	36	15	https://doi.org/10.1101/2020.10.26.351783doi	https://doi.org/10.1101/2020.10.26.351783doi	ADD
10_1101-2020_10_26_351783	36	16	:	:	:
10_1101-2020_10_26_351783	36	17	bioRxiv	biorxiv	IN
10_1101-2020_10_26_351783	36	18	preprint	preprint	NN
10_1101-2020_10_26_351783	36	19	https://doi.org/10.1101/2020.10.26.351783	https://doi.org/10.1101/2020.10.26.351783	CC
10_1101-2020_10_26_351783	36	20	4	4	CD
10_1101-2020_10_26_351783	36	21	In	in	IN
10_1101-2020_10_26_351783	36	22	this	this	DT
10_1101-2020_10_26_351783	36	23	study	study	NN
10_1101-2020_10_26_351783	36	24	we	-PRON-	PRP
10_1101-2020_10_26_351783	36	25	analyzed	analyze	VBD
10_1101-2020_10_26_351783	36	26	,	,	,
10_1101-2020_10_26_351783	36	27	assessed	assess	VBN
10_1101-2020_10_26_351783	36	28	,	,	,
10_1101-2020_10_26_351783	36	29	and	and	CC
10_1101-2020_10_26_351783	36	30	compared	compare	VBD
10_1101-2020_10_26_351783	36	31	the	the	DT
10_1101-2020_10_26_351783	36	32	performance	performance	NN
10_1101-2020_10_26_351783	36	33	of	of	IN
10_1101-2020_10_26_351783	36	34	eight	eight	CD
10_1101-2020_10_26_351783	36	35	of	of	IN
10_1101-2020_10_26_351783	36	36	the	the	DT
10_1101-2020_10_26_351783	36	37	most	most	RBS
10_1101-2020_10_26_351783	36	38	popular	popular	JJ
10_1101-2020_10_26_351783	36	39	methods	method	NNS
10_1101-2020_10_26_351783	36	40	for	for	IN
10_1101-2020_10_26_351783	36	41	disease	disease	NNP
10_1101-2020_10_26_351783	36	42	module	module	NNP
10_1101-2020_10_26_351783	36	43	analysis	analysis	NN
10_1101-2020_10_26_351783	36	44	using	use	VBG
10_1101-2020_10_26_351783	36	45	the	the	DT
10_1101-2020_10_26_351783	36	46	R	r	NN
10_1101-2020_10_26_351783	36	47	package	package	NN
10_1101-2020_10_26_351783	36	48	MODifieR[13	MODifieR[13	NNS
10_1101-2020_10_26_351783	36	49	]	]	-RRB-
10_1101-2020_10_26_351783	36	50	on	on	IN
10_1101-2020_10_26_351783	36	51	19	19	CD
10_1101-2020_10_26_351783	36	52	different	different	JJ
10_1101-2020_10_26_351783	36	53	diseases	disease	NNS
10_1101-2020_10_26_351783	36	54	including	include	VBG
10_1101-2020_10_26_351783	36	55	47	47	CD
10_1101-2020_10_26_351783	36	56	expression	expression	NN
10_1101-2020_10_26_351783	36	57	and	and	CC
10_1101-2020_10_26_351783	36	58	ten	ten	CD
10_1101-2020_10_26_351783	36	59	methylation	methylation	NN
10_1101-2020_10_26_351783	36	60	datasets	dataset	NNS
10_1101-2020_10_26_351783	36	61	.	.	.
10_1101-2020_10_26_351783	37	1	We	-PRON-	PRP
10_1101-2020_10_26_351783	37	2	assessed	assess	VBD
10_1101-2020_10_26_351783	37	3	the	the	DT
10_1101-2020_10_26_351783	37	4	performance	performance	NN
10_1101-2020_10_26_351783	37	5	of	of	IN
10_1101-2020_10_26_351783	37	6	the	the	DT
10_1101-2020_10_26_351783	37	7	methods	method	NNS
10_1101-2020_10_26_351783	37	8	using	use	VBG
10_1101-2020_10_26_351783	37	9	genome	genome	NN
10_1101-2020_10_26_351783	37	10	-	-	HYPH
10_1101-2020_10_26_351783	37	11	wide	wide	JJ
10_1101-2020_10_26_351783	37	12	association	association	NN
10_1101-2020_10_26_351783	37	13	(	(	-LRB-
10_1101-2020_10_26_351783	37	14	GWAS	GWAS	NNP
10_1101-2020_10_26_351783	37	15	)	)	-RRB-
10_1101-2020_10_26_351783	37	16	enrichment	enrichment	NN
10_1101-2020_10_26_351783	37	17	analysis	analysis	NN
10_1101-2020_10_26_351783	37	18	from	from	IN
10_1101-2020_10_26_351783	37	19	the	the	DT
10_1101-2020_10_26_351783	37	20	summary	summary	NN
10_1101-2020_10_26_351783	37	21	statistics	statistic	NNS
10_1101-2020_10_26_351783	37	22	of	of	IN
10_1101-2020_10_26_351783	37	23	all	all	DT
10_1101-2020_10_26_351783	37	24	assayed	assay	VBN
10_1101-2020_10_26_351783	37	25	SNPs	snp	NNS
10_1101-2020_10_26_351783	37	26	similarly	similarly	RB
10_1101-2020_10_26_351783	37	27	as	as	IN
10_1101-2020_10_26_351783	37	28	in	in	IN
10_1101-2020_10_26_351783	37	29	DREAM[12	DREAM[12	''
10_1101-2020_10_26_351783	37	30	]	]	-RRB-
10_1101-2020_10_26_351783	37	31	.	.	.
10_1101-2020_10_26_351783	38	1	The	the	DT
10_1101-2020_10_26_351783	38	2	resulting	result	VBG
10_1101-2020_10_26_351783	38	3	workflow	workflow	NN
10_1101-2020_10_26_351783	38	4	provided	provide	VBD
10_1101-2020_10_26_351783	38	5	a	a	DT
10_1101-2020_10_26_351783	38	6	systematic	systematic	JJ
10_1101-2020_10_26_351783	38	7	procedure	procedure	NN
10_1101-2020_10_26_351783	38	8	for	for	IN
10_1101-2020_10_26_351783	38	9	selecting	select	VBG
10_1101-2020_10_26_351783	38	10	the	the	DT
10_1101-2020_10_26_351783	38	11	best	good	JJS
10_1101-2020_10_26_351783	38	12	method	method	NN
10_1101-2020_10_26_351783	38	13	for	for	IN
10_1101-2020_10_26_351783	38	14	each	each	DT
10_1101-2020_10_26_351783	38	15	disease	disease	NN
10_1101-2020_10_26_351783	38	16	and	and	CC
10_1101-2020_10_26_351783	38	17	set	set	VBD
10_1101-2020_10_26_351783	38	18	the	the	DT
10_1101-2020_10_26_351783	38	19	stage	stage	NN
10_1101-2020_10_26_351783	38	20	for	for	IN
10_1101-2020_10_26_351783	38	21	method	method	NN
10_1101-2020_10_26_351783	38	22	development	development	NN
10_1101-2020_10_26_351783	38	23	in	in	IN
10_1101-2020_10_26_351783	38	24	the	the	DT
10_1101-2020_10_26_351783	38	25	disease	disease	NNP
10_1101-2020_10_26_351783	38	26	module	module	NNP
10_1101-2020_10_26_351783	38	27	area	area	NN
10_1101-2020_10_26_351783	38	28	.	.	.
10_1101-2020_10_26_351783	39	1	Moreover	moreover	RB
10_1101-2020_10_26_351783	39	2	,	,	,
10_1101-2020_10_26_351783	39	3	it	-PRON-	PRP
10_1101-2020_10_26_351783	39	4	allowed	allow	VBD
10_1101-2020_10_26_351783	39	5	the	the	DT
10_1101-2020_10_26_351783	39	6	predictive	predictive	JJ
10_1101-2020_10_26_351783	39	7	assessment	assessment	NN
10_1101-2020_10_26_351783	39	8	of	of	IN
10_1101-2020_10_26_351783	39	9	combining	combine	VBG
10_1101-2020_10_26_351783	39	10	multiple	multiple	JJ
10_1101-2020_10_26_351783	39	11	datasets	dataset	NNS
10_1101-2020_10_26_351783	39	12	across	across	IN
10_1101-2020_10_26_351783	39	13	several	several	JJ
10_1101-2020_10_26_351783	39	14	omics	omic	NNS
10_1101-2020_10_26_351783	39	15	using	use	VBG
10_1101-2020_10_26_351783	39	16	GWAS	GWAS	NNP
10_1101-2020_10_26_351783	39	17	,	,	,
10_1101-2020_10_26_351783	39	18	which	which	WDT
10_1101-2020_10_26_351783	39	19	we	-PRON-	PRP
10_1101-2020_10_26_351783	39	20	tested	test	VBD
10_1101-2020_10_26_351783	39	21	in	in	IN
10_1101-2020_10_26_351783	39	22	multiple	multiple	JJ
10_1101-2020_10_26_351783	39	23	sclerosis	sclerosis	NN
10_1101-2020_10_26_351783	39	24	(	(	-LRB-
10_1101-2020_10_26_351783	39	25	MS	MS	NNP
10_1101-2020_10_26_351783	39	26	)	)	-RRB-
10_1101-2020_10_26_351783	39	27	,	,	,
10_1101-2020_10_26_351783	39	28	a	a	DT
10_1101-2020_10_26_351783	39	29	heterogeneous	heterogeneous	JJ
10_1101-2020_10_26_351783	39	30	complex	complex	JJ
10_1101-2020_10_26_351783	39	31	disease	disease	NN
10_1101-2020_10_26_351783	39	32	.	.	.
10_1101-2020_10_26_351783	40	1	Briefly	briefly	RB
10_1101-2020_10_26_351783	40	2	,	,	,
10_1101-2020_10_26_351783	40	3	we	-PRON-	PRP
10_1101-2020_10_26_351783	40	4	derived	derive	VBD
10_1101-2020_10_26_351783	40	5	multi	multi	JJ
10_1101-2020_10_26_351783	40	6	-	-	JJ
10_1101-2020_10_26_351783	40	7	omic	omic	JJ
10_1101-2020_10_26_351783	40	8	modules	module	NNS
10_1101-2020_10_26_351783	40	9	in	in	IN
10_1101-2020_10_26_351783	40	10	a	a	DT
10_1101-2020_10_26_351783	40	11	stepwise	stepwise	JJ
10_1101-2020_10_26_351783	40	12	optimization	optimization	NN
10_1101-2020_10_26_351783	40	13	of	of	IN
10_1101-2020_10_26_351783	40	14	GWAS	GWAS	NNP
10_1101-2020_10_26_351783	40	15	enrichment	enrichment	NN
10_1101-2020_10_26_351783	40	16	from	from	IN
10_1101-2020_10_26_351783	40	17	transcriptomic	transcriptomic	JJ
10_1101-2020_10_26_351783	40	18	and	and	CC
10_1101-2020_10_26_351783	40	19	methylomic	methylomic	JJ
10_1101-2020_10_26_351783	40	20	analyses	analysis	NNS
10_1101-2020_10_26_351783	40	21	of	of	IN
10_1101-2020_10_26_351783	40	22	MS	MS	NNP
10_1101-2020_10_26_351783	40	23	.	.	.
10_1101-2020_10_26_351783	40	24	We	-PRON-	PRP
10_1101-2020_10_26_351783	40	25	further	further	RB
10_1101-2020_10_26_351783	40	26	evaluated	evaluate	VBD
10_1101-2020_10_26_351783	40	27	the	the	DT
10_1101-2020_10_26_351783	40	28	identified	identify	VBN
10_1101-2020_10_26_351783	40	29	multi	multi	JJ
10_1101-2020_10_26_351783	40	30	-	-	JJ
10_1101-2020_10_26_351783	40	31	omic	omic	JJ
10_1101-2020_10_26_351783	40	32	MS	MS	NNP
10_1101-2020_10_26_351783	40	33	module	module	NN
10_1101-2020_10_26_351783	40	34	of	of	IN
10_1101-2020_10_26_351783	40	35	220	220	CD
10_1101-2020_10_26_351783	40	36	genes	gene	NNS
10_1101-2020_10_26_351783	40	37	for	for	IN
10_1101-2020_10_26_351783	40	38	its	-PRON-	PRP$
10_1101-2020_10_26_351783	40	39	enrichment	enrichment	NN
10_1101-2020_10_26_351783	40	40	across	across	IN
10_1101-2020_10_26_351783	40	41	DNA	dna	NN
10_1101-2020_10_26_351783	40	42	methylation	methylation	NN
10_1101-2020_10_26_351783	40	43	studies	study	NNS
10_1101-2020_10_26_351783	40	44	of	of	IN
10_1101-2020_10_26_351783	40	45	eight	eight	CD
10_1101-2020_10_26_351783	40	46	known	know	VBN
10_1101-2020_10_26_351783	40	47	lifestyle	lifestyle	NN
10_1101-2020_10_26_351783	40	48	-	-	HYPH
10_1101-2020_10_26_351783	40	49	associated	associate	VBN
10_1101-2020_10_26_351783	40	50	risk	risk	NN
10_1101-2020_10_26_351783	40	51	factors	factor	NNS
10_1101-2020_10_26_351783	40	52	of	of	IN
10_1101-2020_10_26_351783	40	53	MS	MS	NNP
10_1101-2020_10_26_351783	40	54	.	.	.
10_1101-2020_10_26_351783	40	55	Additionally	additionally	RB
10_1101-2020_10_26_351783	40	56	,	,	,
10_1101-2020_10_26_351783	40	57	we	-PRON-	PRP
10_1101-2020_10_26_351783	40	58	validated	validate	VBD
10_1101-2020_10_26_351783	40	59	the	the	DT
10_1101-2020_10_26_351783	40	60	identified	identify	VBN
10_1101-2020_10_26_351783	40	61	significant	significant	JJ
10_1101-2020_10_26_351783	40	62	enrichment	enrichment	NN
10_1101-2020_10_26_351783	40	63	risk	risk	NN
10_1101-2020_10_26_351783	40	64	factors	factor	NNS
10_1101-2020_10_26_351783	40	65	in	in	IN
10_1101-2020_10_26_351783	40	66	an	an	DT
10_1101-2020_10_26_351783	40	67	independent	independent	JJ
10_1101-2020_10_26_351783	40	68	DNA	DNA	NNP
10_1101-2020_10_26_351783	40	69	methylation	methylation	NN
10_1101-2020_10_26_351783	40	70	MS	MS	NNP
10_1101-2020_10_26_351783	40	71	study	study	NN
10_1101-2020_10_26_351783	40	72	which	which	WDT
10_1101-2020_10_26_351783	40	73	indeed	indeed	RB
10_1101-2020_10_26_351783	40	74	showed	show	VBD
10_1101-2020_10_26_351783	40	75	a	a	DT
10_1101-2020_10_26_351783	40	76	very	very	RB
10_1101-2020_10_26_351783	40	77	strong	strong	JJ
10_1101-2020_10_26_351783	40	78	and	and	CC
10_1101-2020_10_26_351783	40	79	significant	significant	JJ
10_1101-2020_10_26_351783	40	80	MS	MS	NNP
10_1101-2020_10_26_351783	40	81	enrichment	enrichment	NN
10_1101-2020_10_26_351783	40	82	for	for	IN
10_1101-2020_10_26_351783	40	83	both	both	DT
10_1101-2020_10_26_351783	40	84	module	module	JJ
10_1101-2020_10_26_351783	40	85	genes	gene	NNS
10_1101-2020_10_26_351783	40	86	and	and	CC
10_1101-2020_10_26_351783	40	87	risk	risk	NN
10_1101-2020_10_26_351783	40	88	factor	factor	NN
10_1101-2020_10_26_351783	40	89	associations	association	NNS
10_1101-2020_10_26_351783	40	90	.	.	.
10_1101-2020_10_26_351783	41	1	In	in	IN
10_1101-2020_10_26_351783	41	2	summary	summary	NN
10_1101-2020_10_26_351783	41	3	,	,	,
10_1101-2020_10_26_351783	41	4	we	-PRON-	PRP
10_1101-2020_10_26_351783	41	5	provide	provide	VBP
10_1101-2020_10_26_351783	41	6	a	a	DT
10_1101-2020_10_26_351783	41	7	robust	robust	JJ
10_1101-2020_10_26_351783	41	8	multi	multi	JJ
10_1101-2020_10_26_351783	41	9	-	-	HYPH
10_1101-2020_10_26_351783	41	10	omics	omic	NNS
10_1101-2020_10_26_351783	41	11	strategy	strategy	NN
10_1101-2020_10_26_351783	41	12	that	that	WDT
10_1101-2020_10_26_351783	41	13	can	can	MD
10_1101-2020_10_26_351783	41	14	be	be	VB
10_1101-2020_10_26_351783	41	15	used	use	VBN
10_1101-2020_10_26_351783	41	16	to	to	TO
10_1101-2020_10_26_351783	41	17	disentangle	disentangle	VB
10_1101-2020_10_26_351783	41	18	networks	network	NNS
10_1101-2020_10_26_351783	41	19	of	of	IN
10_1101-2020_10_26_351783	41	20	affected	affect	VBN
10_1101-2020_10_26_351783	41	21	genes	gene	NNS
10_1101-2020_10_26_351783	41	22	in	in	IN
10_1101-2020_10_26_351783	41	23	complex	complex	JJ
10_1101-2020_10_26_351783	41	24	diseases	disease	NNS
10_1101-2020_10_26_351783	41	25	from	from	IN
10_1101-2020_10_26_351783	41	26	both	both	CC
10_1101-2020_10_26_351783	41	27	genetic	genetic	JJ
10_1101-2020_10_26_351783	41	28	and	and	CC
10_1101-2020_10_26_351783	41	29	environmental	environmental	JJ
10_1101-2020_10_26_351783	41	30	levels	level	NNS
10_1101-2020_10_26_351783	41	31	.	.	.
10_1101-2020_10_26_351783	42	1	(	(	-LRB-
10_1101-2020_10_26_351783	42	2	which	which	WDT
10_1101-2020_10_26_351783	42	3	was	be	VBD
10_1101-2020_10_26_351783	42	4	not	not	RB
10_1101-2020_10_26_351783	42	5	certified	certify	VBN
10_1101-2020_10_26_351783	42	6	by	by	IN
10_1101-2020_10_26_351783	42	7	peer	peer	NN
10_1101-2020_10_26_351783	42	8	review	review	NN
10_1101-2020_10_26_351783	42	9	)	)	-RRB-
10_1101-2020_10_26_351783	42	10	is	be	VBZ
10_1101-2020_10_26_351783	42	11	the	the	DT
10_1101-2020_10_26_351783	42	12	author	author	NN
10_1101-2020_10_26_351783	42	13	/	/	SYM
10_1101-2020_10_26_351783	42	14	funder	funder	NN
10_1101-2020_10_26_351783	42	15	.	.	.
10_1101-2020_10_26_351783	43	1	All	all	DT
10_1101-2020_10_26_351783	43	2	rights	right	NNS
10_1101-2020_10_26_351783	43	3	reserved	reserve	VBD
10_1101-2020_10_26_351783	43	4	.	.	.
10_1101-2020_10_26_351783	44	1	No	no	DT
10_1101-2020_10_26_351783	44	2	reuse	reuse	NN
10_1101-2020_10_26_351783	44	3	allowed	allow	VBN
10_1101-2020_10_26_351783	44	4	without	without	IN
10_1101-2020_10_26_351783	44	5	permission	permission	NN
10_1101-2020_10_26_351783	44	6	.	.	.
10_1101-2020_10_26_351783	45	1	The	the	DT
10_1101-2020_10_26_351783	45	2	copyright	copyright	NN
10_1101-2020_10_26_351783	45	3	holder	holder	NN
10_1101-2020_10_26_351783	45	4	for	for	IN
10_1101-2020_10_26_351783	45	5	this	this	DT
10_1101-2020_10_26_351783	45	6	preprintthis	preprintthis	NN
10_1101-2020_10_26_351783	45	7	version	version	NN
10_1101-2020_10_26_351783	45	8	posted	post	VBD
10_1101-2020_10_26_351783	45	9	January	January	NNP
10_1101-2020_10_26_351783	45	10	6	6	CD
10_1101-2020_10_26_351783	45	11	,	,	,
10_1101-2020_10_26_351783	45	12	2021	2021	CD
10_1101-2020_10_26_351783	45	13	.	.	.
10_1101-2020_10_26_351783	45	14	;	;	:
10_1101-2020_10_26_351783	45	15	https://doi.org/10.1101/2020.10.26.351783doi	https://doi.org/10.1101/2020.10.26.351783doi	ADD
10_1101-2020_10_26_351783	45	16	:	:	:
10_1101-2020_10_26_351783	45	17	bioRxiv	biorxiv	IN
10_1101-2020_10_26_351783	45	18	preprint	preprint	NN
10_1101-2020_10_26_351783	45	19	https://doi.org/10.1101/2020.10.26.351783	https://doi.org/10.1101/2020.10.26.351783	CC
10_1101-2020_10_26_351783	45	20	5	5	CD
10_1101-2020_10_26_351783	45	21	MATERIALS	material	NNS
10_1101-2020_10_26_351783	45	22	AND	and	CC
10_1101-2020_10_26_351783	45	23	METHODS	methods	JJ
10_1101-2020_10_26_351783	45	24	Benchmark	benchmark	JJ
10_1101-2020_10_26_351783	45	25	data	datum	NNS
10_1101-2020_10_26_351783	45	26	A	a	DT
10_1101-2020_10_26_351783	45	27	total	total	NN
10_1101-2020_10_26_351783	45	28	of	of	IN
10_1101-2020_10_26_351783	45	29	47	47	CD
10_1101-2020_10_26_351783	45	30	publicly	publicly	RB
10_1101-2020_10_26_351783	45	31	available	available	JJ
10_1101-2020_10_26_351783	45	32	datasets	dataset	NNS
10_1101-2020_10_26_351783	45	33	for	for	IN
10_1101-2020_10_26_351783	45	34	the	the	DT
10_1101-2020_10_26_351783	45	35	transcriptomic	transcriptomic	NNP
10_1101-2020_10_26_351783	45	36	benchmark	benchmark	NN
10_1101-2020_10_26_351783	45	37	and	and	CC
10_1101-2020_10_26_351783	45	38	ten	ten	CD
10_1101-2020_10_26_351783	45	39	publicly	publicly	RB
10_1101-2020_10_26_351783	45	40	available	available	JJ
10_1101-2020_10_26_351783	45	41	datasets	dataset	NNS
10_1101-2020_10_26_351783	45	42	for	for	IN
10_1101-2020_10_26_351783	45	43	the	the	DT
10_1101-2020_10_26_351783	45	44	methylomic	methylomic	JJ
10_1101-2020_10_26_351783	45	45	benchmark	benchmark	NN
10_1101-2020_10_26_351783	45	46	were	be	VBD
10_1101-2020_10_26_351783	45	47	used	use	VBN
10_1101-2020_10_26_351783	45	48	.	.	.
10_1101-2020_10_26_351783	46	1	To	to	TO
10_1101-2020_10_26_351783	46	2	avoid	avoid	VB
10_1101-2020_10_26_351783	46	3	bias	bias	NN
10_1101-2020_10_26_351783	46	4	due	due	IN
10_1101-2020_10_26_351783	46	5	to	to	IN
10_1101-2020_10_26_351783	46	6	subtypes	subtype	NNS
10_1101-2020_10_26_351783	46	7	of	of	IN
10_1101-2020_10_26_351783	46	8	diseases	disease	NNS
10_1101-2020_10_26_351783	46	9	and	and	CC
10_1101-2020_10_26_351783	46	10	drug	drug	NN
10_1101-2020_10_26_351783	46	11	treatments	treatment	NNS
10_1101-2020_10_26_351783	46	12	,	,	,
10_1101-2020_10_26_351783	46	13	we	-PRON-	PRP
10_1101-2020_10_26_351783	46	14	searched	search	VBD
10_1101-2020_10_26_351783	46	15	for	for	IN
10_1101-2020_10_26_351783	46	16	datasets	dataset	NNS
10_1101-2020_10_26_351783	46	17	that	that	WDT
10_1101-2020_10_26_351783	46	18	have	have	VBP
10_1101-2020_10_26_351783	46	19	only	only	JJ
10_1101-2020_10_26_351783	46	20	patient	patient	NN
10_1101-2020_10_26_351783	46	21	and	and	CC
10_1101-2020_10_26_351783	46	22	control	control	NN
10_1101-2020_10_26_351783	46	23	samples	sample	NNS
10_1101-2020_10_26_351783	46	24	,	,	,
10_1101-2020_10_26_351783	46	25	and	and	CC
10_1101-2020_10_26_351783	46	26	that	that	WDT
10_1101-2020_10_26_351783	46	27	are	be	VBP
10_1101-2020_10_26_351783	46	28	available	available	JJ
10_1101-2020_10_26_351783	46	29	for	for	IN
10_1101-2020_10_26_351783	46	30	download	download	NN
10_1101-2020_10_26_351783	46	31	from	from	IN
10_1101-2020_10_26_351783	46	32	the	the	DT
10_1101-2020_10_26_351783	46	33	GEO	GEO	NNP
10_1101-2020_10_26_351783	46	34	database	database	NN
10_1101-2020_10_26_351783	46	35	.	.	.
10_1101-2020_10_26_351783	47	1	We	-PRON-	PRP
10_1101-2020_10_26_351783	47	2	categorized	categorize	VBD
10_1101-2020_10_26_351783	47	3	the	the	DT
10_1101-2020_10_26_351783	47	4	datasets	dataset	NNS
10_1101-2020_10_26_351783	47	5	into	into	IN
10_1101-2020_10_26_351783	47	6	seven	seven	CD
10_1101-2020_10_26_351783	47	7	distinct	distinct	JJ
10_1101-2020_10_26_351783	47	8	disease	disease	NN
10_1101-2020_10_26_351783	47	9	types	type	NNS
10_1101-2020_10_26_351783	47	10	based	base	VBN
10_1101-2020_10_26_351783	47	11	on	on	IN
10_1101-2020_10_26_351783	47	12	the	the	DT
10_1101-2020_10_26_351783	47	13	disease	disease	NN
10_1101-2020_10_26_351783	47	14	-	-	HYPH
10_1101-2020_10_26_351783	47	15	trait	trait	NN
10_1101-2020_10_26_351783	47	16	type	type	NN
10_1101-2020_10_26_351783	47	17	associations	association	NNS
10_1101-2020_10_26_351783	47	18	used	use	VBN
10_1101-2020_10_26_351783	47	19	in	in	IN
10_1101-2020_10_26_351783	47	20	Choobdar	Choobdar	NNP
10_1101-2020_10_26_351783	47	21	et	et	NNP
10_1101-2020_10_26_351783	47	22	al[12	al[12	NNP
10_1101-2020_10_26_351783	47	23	]	]	-RRB-
10_1101-2020_10_26_351783	47	24	.	.	.
10_1101-2020_10_26_351783	47	25	,	,	,
10_1101-2020_10_26_351783	47	26	i.e.	i.e.	FW
10_1101-2020_10_26_351783	48	1	autoimmune	autoimmune	NNP
10_1101-2020_10_26_351783	48	2	,	,	,
10_1101-2020_10_26_351783	48	3	cardiovascular	cardiovascular	NNP
10_1101-2020_10_26_351783	48	4	,	,	,
10_1101-2020_10_26_351783	48	5	glycemic	glycemic	JJ
10_1101-2020_10_26_351783	48	6	,	,	,
10_1101-2020_10_26_351783	48	7	inflammatory	inflammatory	JJ
10_1101-2020_10_26_351783	48	8	,	,	,
10_1101-2020_10_26_351783	48	9	neurodegenerative	neurodegenerative	JJ
10_1101-2020_10_26_351783	48	10	,	,	,
10_1101-2020_10_26_351783	48	11	and	and	CC
10_1101-2020_10_26_351783	48	12	psychiatric	psychiatric	JJ
10_1101-2020_10_26_351783	48	13	and	and	CC
10_1101-2020_10_26_351783	48	14	social	social	JJ
10_1101-2020_10_26_351783	48	15	disorders	disorder	NNS
10_1101-2020_10_26_351783	48	16	.	.	.
10_1101-2020_10_26_351783	49	1	A	a	DT
10_1101-2020_10_26_351783	49	2	total	total	NN
10_1101-2020_10_26_351783	49	3	of	of	IN
10_1101-2020_10_26_351783	49	4	19	19	CD
10_1101-2020_10_26_351783	49	5	complex	complex	JJ
10_1101-2020_10_26_351783	49	6	diseases	disease	NNS
10_1101-2020_10_26_351783	49	7	were	be	VBD
10_1101-2020_10_26_351783	49	8	used	use	VBN
10_1101-2020_10_26_351783	49	9	in	in	IN
10_1101-2020_10_26_351783	49	10	the	the	DT
10_1101-2020_10_26_351783	49	11	transcriptomic	transcriptomic	NNP
10_1101-2020_10_26_351783	49	12	benchmark	benchmark	NN
10_1101-2020_10_26_351783	49	13	analysis	analysis	NN
10_1101-2020_10_26_351783	49	14	,	,	,
10_1101-2020_10_26_351783	49	15	while	while	IN
10_1101-2020_10_26_351783	49	16	six	six	CD
10_1101-2020_10_26_351783	49	17	complex	complex	JJ
10_1101-2020_10_26_351783	49	18	diseases	disease	NNS
10_1101-2020_10_26_351783	49	19	were	be	VBD
10_1101-2020_10_26_351783	49	20	used	use	VBN
10_1101-2020_10_26_351783	49	21	in	in	IN
10_1101-2020_10_26_351783	49	22	the	the	DT
10_1101-2020_10_26_351783	49	23	methylation	methylation	NN
10_1101-2020_10_26_351783	49	24	benchmark	benchmark	NN
10_1101-2020_10_26_351783	49	25	analysis	analysis	NN
10_1101-2020_10_26_351783	49	26	.	.	.
10_1101-2020_10_26_351783	50	1	The	the	DT
10_1101-2020_10_26_351783	50	2	methylation	methylation	NN
10_1101-2020_10_26_351783	50	3	benchmark	benchmark	NN
10_1101-2020_10_26_351783	50	4	diseases	disease	NNS
10_1101-2020_10_26_351783	50	5	belong	belong	VBP
10_1101-2020_10_26_351783	50	6	to	to	IN
10_1101-2020_10_26_351783	50	7	inflammatory	inflammatory	JJ
10_1101-2020_10_26_351783	50	8	,	,	,
10_1101-2020_10_26_351783	50	9	autoimmune	autoimmune	NNP
10_1101-2020_10_26_351783	50	10	,	,	,
10_1101-2020_10_26_351783	50	11	and	and	CC
10_1101-2020_10_26_351783	50	12	glycemic	glycemic	JJ
10_1101-2020_10_26_351783	50	13	disease	disease	NN
10_1101-2020_10_26_351783	50	14	types	type	NNS
10_1101-2020_10_26_351783	50	15	.	.	.
10_1101-2020_10_26_351783	51	1	MS	MS	NNP
10_1101-2020_10_26_351783	51	2	use	use	VBP
10_1101-2020_10_26_351783	51	3	case	case	NN
10_1101-2020_10_26_351783	51	4	data	datum	NNS
10_1101-2020_10_26_351783	51	5	A	a	DT
10_1101-2020_10_26_351783	51	6	total	total	NN
10_1101-2020_10_26_351783	51	7	of	of	IN
10_1101-2020_10_26_351783	51	8	14	14	CD
10_1101-2020_10_26_351783	51	9	publicly	publicly	RB
10_1101-2020_10_26_351783	51	10	available	available	JJ
10_1101-2020_10_26_351783	51	11	and	and	CC
10_1101-2020_10_26_351783	51	12	one	one	CD
10_1101-2020_10_26_351783	51	13	non	non	JJ
10_1101-2020_10_26_351783	51	14	-	-	JJ
10_1101-2020_10_26_351783	51	15	publicly	publicly	RB
10_1101-2020_10_26_351783	51	16	available	available	JJ
10_1101-2020_10_26_351783	51	17	transcriptomic	transcriptomic	NN
10_1101-2020_10_26_351783	51	18	and	and	CC
10_1101-2020_10_26_351783	51	19	methylomic	methylomic	JJ
10_1101-2020_10_26_351783	51	20	MS-	ms-	JJ
10_1101-2020_10_26_351783	51	21	related	relate	VBN
10_1101-2020_10_26_351783	51	22	datasets	dataset	NNS
10_1101-2020_10_26_351783	51	23	were	be	VBD
10_1101-2020_10_26_351783	51	24	used	use	VBN
10_1101-2020_10_26_351783	51	25	in	in	IN
10_1101-2020_10_26_351783	51	26	the	the	DT
10_1101-2020_10_26_351783	51	27	MS	MS	NNP
10_1101-2020_10_26_351783	51	28	multi	multi	JJ
10_1101-2020_10_26_351783	51	29	-	-	HYPH
10_1101-2020_10_26_351783	51	30	omics	omic	NNS
10_1101-2020_10_26_351783	51	31	integration	integration	NN
10_1101-2020_10_26_351783	51	32	use	use	NN
10_1101-2020_10_26_351783	51	33	case	case	NN
10_1101-2020_10_26_351783	51	34	.	.	.
10_1101-2020_10_26_351783	52	1	In	in	IN
10_1101-2020_10_26_351783	52	2	general	general	JJ
10_1101-2020_10_26_351783	52	3	,	,	,
10_1101-2020_10_26_351783	52	4	every	every	DT
10_1101-2020_10_26_351783	52	5	dataset	dataset	NN
10_1101-2020_10_26_351783	52	6	in	in	IN
10_1101-2020_10_26_351783	52	7	the	the	DT
10_1101-2020_10_26_351783	52	8	MODifieR	MODifieR	NNP
10_1101-2020_10_26_351783	52	9	benchmark	benchmark	NN
10_1101-2020_10_26_351783	52	10	was	be	VBD
10_1101-2020_10_26_351783	52	11	also	also	RB
10_1101-2020_10_26_351783	52	12	used	use	VBN
10_1101-2020_10_26_351783	52	13	in	in	IN
10_1101-2020_10_26_351783	52	14	the	the	DT
10_1101-2020_10_26_351783	52	15	MS	MS	NNP
10_1101-2020_10_26_351783	52	16	use	use	NN
10_1101-2020_10_26_351783	52	17	case	case	NN
10_1101-2020_10_26_351783	52	18	,	,	,
10_1101-2020_10_26_351783	52	19	with	with	IN
10_1101-2020_10_26_351783	52	20	exceptions	exception	NNS
10_1101-2020_10_26_351783	52	21	according	accord	VBG
10_1101-2020_10_26_351783	52	22	to	to	IN
10_1101-2020_10_26_351783	52	23	certain	certain	JJ
10_1101-2020_10_26_351783	52	24	criteria	criterion	NNS
10_1101-2020_10_26_351783	52	25	.	.	.
10_1101-2020_10_26_351783	53	1	The	the	DT
10_1101-2020_10_26_351783	53	2	inclusion	inclusion	NN
10_1101-2020_10_26_351783	53	3	of	of	IN
10_1101-2020_10_26_351783	53	4	transcriptomic	transcriptomic	JJ
10_1101-2020_10_26_351783	53	5	MS	MS	NNP
10_1101-2020_10_26_351783	53	6	datasets	dataset	NNS
10_1101-2020_10_26_351783	53	7	followed	follow	VBD
10_1101-2020_10_26_351783	53	8	the	the	DT
10_1101-2020_10_26_351783	53	9	criteria	criterion	NNS
10_1101-2020_10_26_351783	53	10	:	:	:
10_1101-2020_10_26_351783	53	11	1	1	LS
10_1101-2020_10_26_351783	53	12	)	)	-RRB-
10_1101-2020_10_26_351783	53	13	The	the	DT
10_1101-2020_10_26_351783	53	14	largest	large	JJS
10_1101-2020_10_26_351783	53	15	dataset	dataset	NN
10_1101-2020_10_26_351783	53	16	by	by	IN
10_1101-2020_10_26_351783	53	17	sample	sample	NN
10_1101-2020_10_26_351783	53	18	number	number	NN
10_1101-2020_10_26_351783	53	19	,	,	,
10_1101-2020_10_26_351783	53	20	per	per	IN
10_1101-2020_10_26_351783	53	21	tissue	tissue	NN
10_1101-2020_10_26_351783	53	22	,	,	,
10_1101-2020_10_26_351783	53	23	is	be	VBZ
10_1101-2020_10_26_351783	53	24	shown	show	VBN
10_1101-2020_10_26_351783	53	25	in	in	IN
10_1101-2020_10_26_351783	53	26	the	the	DT
10_1101-2020_10_26_351783	53	27	MODifieR	MODifieR	NNP
10_1101-2020_10_26_351783	53	28	benchmark	benchmark	NN
10_1101-2020_10_26_351783	53	29	;	;	:
10_1101-2020_10_26_351783	53	30	2	2	LS
10_1101-2020_10_26_351783	53	31	)	)	-RRB-
10_1101-2020_10_26_351783	53	32	Replication	replication	NN
10_1101-2020_10_26_351783	53	33	cohorts	cohort	NNS
10_1101-2020_10_26_351783	53	34	are	be	VBP
10_1101-2020_10_26_351783	53	35	not	not	RB
10_1101-2020_10_26_351783	53	36	included	include	VBN
10_1101-2020_10_26_351783	53	37	in	in	IN
10_1101-2020_10_26_351783	53	38	the	the	DT
10_1101-2020_10_26_351783	53	39	MS	MS	NNP
10_1101-2020_10_26_351783	53	40	use	use	NN
10_1101-2020_10_26_351783	53	41	case	case	NN
10_1101-2020_10_26_351783	53	42	.	.	.
10_1101-2020_10_26_351783	54	1	Criteria	criterion	NNS
10_1101-2020_10_26_351783	54	2	for	for	IN
10_1101-2020_10_26_351783	54	3	inclusion	inclusion	NN
10_1101-2020_10_26_351783	54	4	of	of	IN
10_1101-2020_10_26_351783	54	5	methylomic	methylomic	JJ
10_1101-2020_10_26_351783	54	6	MS	MS	NNP
10_1101-2020_10_26_351783	54	7	datasets	dataset	NNS
10_1101-2020_10_26_351783	54	8	were	be	VBD
10_1101-2020_10_26_351783	54	9	the	the	DT
10_1101-2020_10_26_351783	54	10	following	follow	VBG
10_1101-2020_10_26_351783	54	11	:	:	:
10_1101-2020_10_26_351783	54	12	1	1	LS
10_1101-2020_10_26_351783	54	13	)	)	-RRB-
10_1101-2020_10_26_351783	54	14	The	the	DT
10_1101-2020_10_26_351783	54	15	largest	large	JJS
10_1101-2020_10_26_351783	54	16	dataset	dataset	NN
10_1101-2020_10_26_351783	54	17	by	by	IN
10_1101-2020_10_26_351783	54	18	sample	sample	NN
10_1101-2020_10_26_351783	54	19	number	number	NN
10_1101-2020_10_26_351783	54	20	,	,	,
10_1101-2020_10_26_351783	54	21	per	per	IN
10_1101-2020_10_26_351783	54	22	tissue	tissue	NN
10_1101-2020_10_26_351783	54	23	or	or	CC
10_1101-2020_10_26_351783	54	24	cell	cell	NN
10_1101-2020_10_26_351783	54	25	type	type	NN
10_1101-2020_10_26_351783	54	26	,	,	,
10_1101-2020_10_26_351783	54	27	is	be	VBZ
10_1101-2020_10_26_351783	54	28	included	include	VBN
10_1101-2020_10_26_351783	54	29	in	in	IN
10_1101-2020_10_26_351783	54	30	the	the	DT
10_1101-2020_10_26_351783	54	31	MODifieR	MODifieR	NNP
10_1101-2020_10_26_351783	54	32	benchmark	benchmark	NN
10_1101-2020_10_26_351783	54	33	;	;	:
10_1101-2020_10_26_351783	54	34	2	2	LS
10_1101-2020_10_26_351783	54	35	)	)	-RRB-
10_1101-2020_10_26_351783	54	36	A	a	DT
10_1101-2020_10_26_351783	54	37	single	single	JJ
10_1101-2020_10_26_351783	54	38	dataset	dataset	NN
10_1101-2020_10_26_351783	54	39	for	for	IN
10_1101-2020_10_26_351783	54	40	every	every	DT
10_1101-2020_10_26_351783	54	41	cell	cell	NN
10_1101-2020_10_26_351783	54	42	-	-	HYPH
10_1101-2020_10_26_351783	54	43	specific	specific	JJ
10_1101-2020_10_26_351783	54	44	tissue	tissue	NN
10_1101-2020_10_26_351783	54	45	was	be	VBD
10_1101-2020_10_26_351783	54	46	included	include	VBN
10_1101-2020_10_26_351783	54	47	in	in	IN
10_1101-2020_10_26_351783	54	48	the	the	DT
10_1101-2020_10_26_351783	54	49	benchmark	benchmark	NN
10_1101-2020_10_26_351783	54	50	;	;	:
10_1101-2020_10_26_351783	54	51	3	3	LS
10_1101-2020_10_26_351783	54	52	)	)	-RRB-
10_1101-2020_10_26_351783	54	53	Methylation	methylation	NN
10_1101-2020_10_26_351783	54	54	studies	study	NNS
10_1101-2020_10_26_351783	54	55	that	that	WDT
10_1101-2020_10_26_351783	54	56	reported	report	VBD
10_1101-2020_10_26_351783	54	57	using	use	VBG
10_1101-2020_10_26_351783	54	58	whole	whole	JJ
10_1101-2020_10_26_351783	54	59	blood	blood	NN
10_1101-2020_10_26_351783	54	60	as	as	IN
10_1101-2020_10_26_351783	54	61	sample	sample	NN
10_1101-2020_10_26_351783	54	62	tissue	tissue	NN
10_1101-2020_10_26_351783	54	63	were	be	VBD
10_1101-2020_10_26_351783	54	64	excluded	exclude	VBN
10_1101-2020_10_26_351783	54	65	from	from	IN
10_1101-2020_10_26_351783	54	66	the	the	DT
10_1101-2020_10_26_351783	54	67	MS	MS	NNP
10_1101-2020_10_26_351783	54	68	use	use	NN
10_1101-2020_10_26_351783	54	69	case	case	NN
10_1101-2020_10_26_351783	54	70	,	,	,
10_1101-2020_10_26_351783	54	71	due	due	IN
10_1101-2020_10_26_351783	54	72	to	to	IN
10_1101-2020_10_26_351783	54	73	the	the	DT
10_1101-2020_10_26_351783	54	74	high	high	JJ
10_1101-2020_10_26_351783	54	75	heterogeneity	heterogeneity	NN
10_1101-2020_10_26_351783	54	76	of	of	IN
10_1101-2020_10_26_351783	54	77	this	this	DT
10_1101-2020_10_26_351783	54	78	type	type	NN
10_1101-2020_10_26_351783	54	79	of	of	IN
10_1101-2020_10_26_351783	54	80	data	datum	NNS
10_1101-2020_10_26_351783	54	81	.	.	.
10_1101-2020_10_26_351783	55	1	For	for	IN
10_1101-2020_10_26_351783	55	2	the	the	DT
10_1101-2020_10_26_351783	55	3	additional	additional	JJ
10_1101-2020_10_26_351783	55	4	independent	independent	JJ
10_1101-2020_10_26_351783	55	5	validation	validation	NN
10_1101-2020_10_26_351783	55	6	,	,	,
10_1101-2020_10_26_351783	55	7	we	-PRON-	PRP
10_1101-2020_10_26_351783	55	8	utilized	utilize	VBD
10_1101-2020_10_26_351783	55	9	the	the	DT
10_1101-2020_10_26_351783	55	10	methylation	methylation	NN
10_1101-2020_10_26_351783	55	11	microarray	microarray	JJ
10_1101-2020_10_26_351783	55	12	analysis	analysis	NN
10_1101-2020_10_26_351783	55	13	of	of	IN
10_1101-2020_10_26_351783	55	14	279	279	CD
10_1101-2020_10_26_351783	55	15	blood	blood	NN
10_1101-2020_10_26_351783	55	16	samples	sample	NNS
10_1101-2020_10_26_351783	55	17	analyzing	analyze	VBG
10_1101-2020_10_26_351783	55	18	from	from	IN
10_1101-2020_10_26_351783	55	19	Kular	Kular	NNP
10_1101-2020_10_26_351783	55	20	et	et	NNP
10_1101-2020_10_26_351783	55	21	al	al	NNP
10_1101-2020_10_26_351783	55	22	25	25	CD
10_1101-2020_10_26_351783	55	23	.	.	.
10_1101-2020_10_26_351783	56	1	For	for	IN
10_1101-2020_10_26_351783	56	2	each	each	DT
10_1101-2020_10_26_351783	56	3	of	of	IN
10_1101-2020_10_26_351783	56	4	these	these	DT
10_1101-2020_10_26_351783	56	5	MS	MS	NNP
10_1101-2020_10_26_351783	56	6	patients	patient	NNS
10_1101-2020_10_26_351783	56	7	(	(	-LRB-
10_1101-2020_10_26_351783	56	8	nMS=	nMS=	NNP
10_1101-2020_10_26_351783	56	9	139	139	CD
10_1101-2020_10_26_351783	56	10	)	)	-RRB-
10_1101-2020_10_26_351783	56	11	and	and	CC
10_1101-2020_10_26_351783	56	12	healthy	healthy	JJ
10_1101-2020_10_26_351783	56	13	controls	control	NNS
10_1101-2020_10_26_351783	56	14	(	(	-LRB-
10_1101-2020_10_26_351783	56	15	nHC=	nhc=	IN
10_1101-2020_10_26_351783	56	16	140	140	CD
10_1101-2020_10_26_351783	56	17	)	)	-RRB-
10_1101-2020_10_26_351783	56	18	,	,	,
10_1101-2020_10_26_351783	56	19	we	-PRON-	PRP
10_1101-2020_10_26_351783	56	20	also	also	RB
10_1101-2020_10_26_351783	56	21	collected	collect	VBD
10_1101-2020_10_26_351783	56	22	their	-PRON-	PRP$
10_1101-2020_10_26_351783	56	23	lifestyle	lifestyle	NN
10_1101-2020_10_26_351783	56	24	-	-	HYPH
10_1101-2020_10_26_351783	56	25	associated	associate	VBN
10_1101-2020_10_26_351783	56	26	risk	risk	NN
10_1101-2020_10_26_351783	56	27	factors	factor	NNS
10_1101-2020_10_26_351783	56	28	from	from	IN
10_1101-2020_10_26_351783	56	29	questionnaires	questionnaire	NNS
10_1101-2020_10_26_351783	56	30	that	that	WDT
10_1101-2020_10_26_351783	56	31	(	(	-LRB-
10_1101-2020_10_26_351783	56	32	which	which	WDT
10_1101-2020_10_26_351783	56	33	was	be	VBD
10_1101-2020_10_26_351783	56	34	not	not	RB
10_1101-2020_10_26_351783	56	35	certified	certify	VBN
10_1101-2020_10_26_351783	56	36	by	by	IN
10_1101-2020_10_26_351783	56	37	peer	peer	NN
10_1101-2020_10_26_351783	56	38	review	review	NN
10_1101-2020_10_26_351783	56	39	)	)	-RRB-
10_1101-2020_10_26_351783	56	40	is	be	VBZ
10_1101-2020_10_26_351783	56	41	the	the	DT
10_1101-2020_10_26_351783	56	42	author	author	NN
10_1101-2020_10_26_351783	56	43	/	/	SYM
10_1101-2020_10_26_351783	56	44	funder	funder	NN
10_1101-2020_10_26_351783	56	45	.	.	.
10_1101-2020_10_26_351783	57	1	All	all	DT
10_1101-2020_10_26_351783	57	2	rights	right	NNS
10_1101-2020_10_26_351783	57	3	reserved	reserve	VBD
10_1101-2020_10_26_351783	57	4	.	.	.
10_1101-2020_10_26_351783	58	1	No	no	DT
10_1101-2020_10_26_351783	58	2	reuse	reuse	NN
10_1101-2020_10_26_351783	58	3	allowed	allow	VBN
10_1101-2020_10_26_351783	58	4	without	without	IN
10_1101-2020_10_26_351783	58	5	permission	permission	NN
10_1101-2020_10_26_351783	58	6	.	.	.
10_1101-2020_10_26_351783	59	1	The	the	DT
10_1101-2020_10_26_351783	59	2	copyright	copyright	NN
10_1101-2020_10_26_351783	59	3	holder	holder	NN
10_1101-2020_10_26_351783	59	4	for	for	IN
10_1101-2020_10_26_351783	59	5	this	this	DT
10_1101-2020_10_26_351783	59	6	preprintthis	preprintthis	NN
10_1101-2020_10_26_351783	59	7	version	version	NN
10_1101-2020_10_26_351783	59	8	posted	post	VBD
10_1101-2020_10_26_351783	59	9	January	January	NNP
10_1101-2020_10_26_351783	59	10	6	6	CD
10_1101-2020_10_26_351783	59	11	,	,	,
10_1101-2020_10_26_351783	59	12	2021	2021	CD
10_1101-2020_10_26_351783	59	13	.	.	.
10_1101-2020_10_26_351783	59	14	;	;	:
10_1101-2020_10_26_351783	59	15	https://doi.org/10.1101/2020.10.26.351783doi	https://doi.org/10.1101/2020.10.26.351783doi	ADD
10_1101-2020_10_26_351783	59	16	:	:	:
10_1101-2020_10_26_351783	59	17	bioRxiv	biorxiv	IN
10_1101-2020_10_26_351783	59	18	preprint	preprint	NN
10_1101-2020_10_26_351783	59	19	https://doi.org/10.1101/2020.10.26.351783	https://doi.org/10.1101/2020.10.26.351783	NNP
10_1101-2020_10_26_351783	59	20	6	6	CD
10_1101-2020_10_26_351783	59	21	were	be	VBD
10_1101-2020_10_26_351783	59	22	part	part	NN
10_1101-2020_10_26_351783	59	23	of	of	IN
10_1101-2020_10_26_351783	59	24	the	the	DT
10_1101-2020_10_26_351783	59	25	Epidemiological	Epidemiological	NNP
10_1101-2020_10_26_351783	59	26	Investigation	Investigation	NNP
10_1101-2020_10_26_351783	59	27	of	of	IN
10_1101-2020_10_26_351783	59	28	Multiple	Multiple	NNP
10_1101-2020_10_26_351783	59	29	Sclerosis	Sclerosis	NNP
10_1101-2020_10_26_351783	59	30	(	(	-LRB-
10_1101-2020_10_26_351783	59	31	EIMS	eims	NN
10_1101-2020_10_26_351783	59	32	)	)	-RRB-
10_1101-2020_10_26_351783	59	33	study	study	NN
10_1101-2020_10_26_351783	59	34	.	.	.
10_1101-2020_10_26_351783	60	1	Those	those	DT
10_1101-2020_10_26_351783	60	2	factors	factor	NNS
10_1101-2020_10_26_351783	60	3	were	be	VBD
10_1101-2020_10_26_351783	60	4	smoking	smoke	VBG
10_1101-2020_10_26_351783	60	5	status	status	NN
10_1101-2020_10_26_351783	60	6	,	,	,
10_1101-2020_10_26_351783	60	7	prior	prior	RB
10_1101-2020_10_26_351783	60	8	EBV	EBV	NNP
10_1101-2020_10_26_351783	60	9	infection	infection	NN
10_1101-2020_10_26_351783	60	10	,	,	,
10_1101-2020_10_26_351783	60	11	sunbathing	sunbathing	NN
10_1101-2020_10_26_351783	60	12	,	,	,
10_1101-2020_10_26_351783	60	13	nightshift	nightshift	JJ
10_1101-2020_10_26_351783	60	14	work	work	NN
10_1101-2020_10_26_351783	60	15	,	,	,
10_1101-2020_10_26_351783	60	16	alcohol	alcohol	NN
10_1101-2020_10_26_351783	60	17	consumption	consumption	NN
10_1101-2020_10_26_351783	60	18	,	,	,
10_1101-2020_10_26_351783	60	19	as	as	RB
10_1101-2020_10_26_351783	60	20	well	well	RB
10_1101-2020_10_26_351783	60	21	as	as	IN
10_1101-2020_10_26_351783	60	22	phenotypic	phenotypic	NN
10_1101-2020_10_26_351783	60	23	features	feature	NNS
10_1101-2020_10_26_351783	60	24	(	(	-LRB-
10_1101-2020_10_26_351783	60	25	age	age	NN
10_1101-2020_10_26_351783	60	26	,	,	,
10_1101-2020_10_26_351783	60	27	sex	sex	NN
10_1101-2020_10_26_351783	60	28	,	,	,
10_1101-2020_10_26_351783	60	29	BMI	BMI	NNP
10_1101-2020_10_26_351783	60	30	at	at	IN
10_1101-2020_10_26_351783	60	31	age	age	NN
10_1101-2020_10_26_351783	60	32	of	of	IN
10_1101-2020_10_26_351783	60	33	20	20	CD
10_1101-2020_10_26_351783	60	34	)	)	-RRB-
10_1101-2020_10_26_351783	60	35	.	.	.
10_1101-2020_10_26_351783	61	1	Pre	pre	JJ
10_1101-2020_10_26_351783	61	2	-	-	JJ
10_1101-2020_10_26_351783	61	3	processing	processing	NN
10_1101-2020_10_26_351783	61	4	and	and	CC
10_1101-2020_10_26_351783	61	5	quality	quality	NN
10_1101-2020_10_26_351783	61	6	control	control	NN
10_1101-2020_10_26_351783	61	7	of	of	IN
10_1101-2020_10_26_351783	61	8	risk	risk	NN
10_1101-2020_10_26_351783	61	9	factor	factor	NN
10_1101-2020_10_26_351783	61	10	methylation	methylation	NN
10_1101-2020_10_26_351783	61	11	data	datum	NNS
10_1101-2020_10_26_351783	61	12	DNA	dna	NN
10_1101-2020_10_26_351783	61	13	methylation	methylation	NN
10_1101-2020_10_26_351783	61	14	datasets	dataset	NNS
10_1101-2020_10_26_351783	61	15	were	be	VBD
10_1101-2020_10_26_351783	61	16	downloaded	download	VBN
10_1101-2020_10_26_351783	61	17	from	from	IN
10_1101-2020_10_26_351783	61	18	GEO	GEO	NNP
10_1101-2020_10_26_351783	61	19	as	as	IN
10_1101-2020_10_26_351783	61	20	raw	raw	JJ
10_1101-2020_10_26_351783	61	21	IDAT	IDAT	NNP
10_1101-2020_10_26_351783	61	22	files	file	NNS
10_1101-2020_10_26_351783	61	23	,	,	,
10_1101-2020_10_26_351783	61	24	when	when	WRB
10_1101-2020_10_26_351783	61	25	available	available	JJ
10_1101-2020_10_26_351783	61	26	,	,	,
10_1101-2020_10_26_351783	61	27	or	or	CC
10_1101-2020_10_26_351783	61	28	matrices	matrix	NNS
10_1101-2020_10_26_351783	61	29	of	of	IN
10_1101-2020_10_26_351783	61	30	beta	beta	JJ
10_1101-2020_10_26_351783	61	31	values	value	NNS
10_1101-2020_10_26_351783	61	32	.	.	.
10_1101-2020_10_26_351783	62	1	Pre	pre	JJ
10_1101-2020_10_26_351783	62	2	-	-	JJ
10_1101-2020_10_26_351783	62	3	processing	processing	NN
10_1101-2020_10_26_351783	62	4	of	of	IN
10_1101-2020_10_26_351783	62	5	the	the	DT
10_1101-2020_10_26_351783	62	6	data	datum	NNS
10_1101-2020_10_26_351783	62	7	was	be	VBD
10_1101-2020_10_26_351783	62	8	performed	perform	VBN
10_1101-2020_10_26_351783	62	9	using	use	VBG
10_1101-2020_10_26_351783	62	10	the	the	DT
10_1101-2020_10_26_351783	62	11	Chip	Chip	NNP
10_1101-2020_10_26_351783	62	12	Analysis	Analysis	NNP
10_1101-2020_10_26_351783	62	13	Methylation	Methylation	NNP
10_1101-2020_10_26_351783	62	14	Pipeline	Pipeline	NNP
10_1101-2020_10_26_351783	62	15	(	(	-LRB-
10_1101-2020_10_26_351783	62	16	ChAMP	ChAMP	NNP
10_1101-2020_10_26_351783	62	17	)	)	-RRB-
10_1101-2020_10_26_351783	62	18	R	R	NNP
10_1101-2020_10_26_351783	62	19	package[14	package[14	RB
10_1101-2020_10_26_351783	62	20	]	]	-RRB-
10_1101-2020_10_26_351783	62	21	,	,	,
10_1101-2020_10_26_351783	62	22	version	version	NN
10_1101-2020_10_26_351783	62	23	2.16.2	2.16.2	CD
10_1101-2020_10_26_351783	62	24	.	.	.
10_1101-2020_10_26_351783	63	1	Default	default	NN
10_1101-2020_10_26_351783	63	2	parameters	parameter	NNS
10_1101-2020_10_26_351783	63	3	were	be	VBD
10_1101-2020_10_26_351783	63	4	used	use	VBN
10_1101-2020_10_26_351783	63	5	for	for	IN
10_1101-2020_10_26_351783	63	6	probe	probe	NN
10_1101-2020_10_26_351783	63	7	and	and	CC
10_1101-2020_10_26_351783	63	8	sample	sample	NN
10_1101-2020_10_26_351783	63	9	filtering	filtering	NN
10_1101-2020_10_26_351783	63	10	.	.	.
10_1101-2020_10_26_351783	64	1	Probes	probe	NNS
10_1101-2020_10_26_351783	64	2	with	with	IN
10_1101-2020_10_26_351783	64	3	a	a	DT
10_1101-2020_10_26_351783	64	4	detection	detection	NN
10_1101-2020_10_26_351783	64	5	P	p	NN
10_1101-2020_10_26_351783	64	6	-	-	HYPH
10_1101-2020_10_26_351783	64	7	value	value	NN
10_1101-2020_10_26_351783	64	8	above	above	IN
10_1101-2020_10_26_351783	64	9	0.01	0.01	CD
10_1101-2020_10_26_351783	64	10	,	,	,
10_1101-2020_10_26_351783	64	11	probes	probe	NNS
10_1101-2020_10_26_351783	64	12	with	with	IN
10_1101-2020_10_26_351783	64	13	a	a	DT
10_1101-2020_10_26_351783	64	14	fraction	fraction	NN
10_1101-2020_10_26_351783	64	15	of	of	IN
10_1101-2020_10_26_351783	64	16	failed	fail	VBN
10_1101-2020_10_26_351783	64	17	(	(	-LRB-
10_1101-2020_10_26_351783	64	18	bead	bead	NNP
10_1101-2020_10_26_351783	64	19	count	count	NNP
10_1101-2020_10_26_351783	64	20	less	less	JJR
10_1101-2020_10_26_351783	64	21	than	than	IN
10_1101-2020_10_26_351783	64	22	3	3	CD
10_1101-2020_10_26_351783	64	23	)	)	-RRB-
10_1101-2020_10_26_351783	64	24	samples	sample	NNS
10_1101-2020_10_26_351783	64	25	over	over	IN
10_1101-2020_10_26_351783	64	26	0.05	0.05	CD
10_1101-2020_10_26_351783	64	27	,	,	,
10_1101-2020_10_26_351783	64	28	non	non	JJ
10_1101-2020_10_26_351783	64	29	-	-	JJ
10_1101-2020_10_26_351783	64	30	CpG	cpg	JJ
10_1101-2020_10_26_351783	64	31	probes	probe	NNS
10_1101-2020_10_26_351783	64	32	,	,	,
10_1101-2020_10_26_351783	64	33	SNP	SNP	NNP
10_1101-2020_10_26_351783	64	34	-	-	HYPH
10_1101-2020_10_26_351783	64	35	related	relate	VBN
10_1101-2020_10_26_351783	64	36	probes	probe	NNS
10_1101-2020_10_26_351783	64	37	,	,	,
10_1101-2020_10_26_351783	64	38	multi	multi	JJ
10_1101-2020_10_26_351783	64	39	-	-	JJ
10_1101-2020_10_26_351783	64	40	hit	hit	VBN
10_1101-2020_10_26_351783	64	41	probes	probe	NNS
10_1101-2020_10_26_351783	64	42	,	,	,
10_1101-2020_10_26_351783	64	43	and	and	CC
10_1101-2020_10_26_351783	64	44	probes	probe	NNS
10_1101-2020_10_26_351783	64	45	located	locate	VBN
10_1101-2020_10_26_351783	64	46	on	on	IN
10_1101-2020_10_26_351783	64	47	chromosomes	chromosome	NNS
10_1101-2020_10_26_351783	64	48	X	X	NNP
10_1101-2020_10_26_351783	64	49	and	and	CC
10_1101-2020_10_26_351783	64	50	Y	Y	NNP
10_1101-2020_10_26_351783	64	51	,	,	,
10_1101-2020_10_26_351783	64	52	were	be	VBD
10_1101-2020_10_26_351783	64	53	removed	remove	VBN
10_1101-2020_10_26_351783	64	54	.	.	.
10_1101-2020_10_26_351783	65	1	Samples	sample	NNS
10_1101-2020_10_26_351783	65	2	with	with	IN
10_1101-2020_10_26_351783	65	3	a	a	DT
10_1101-2020_10_26_351783	65	4	proportion	proportion	NN
10_1101-2020_10_26_351783	65	5	of	of	IN
10_1101-2020_10_26_351783	65	6	failed	fail	VBN
10_1101-2020_10_26_351783	65	7	(	(	-LRB-
10_1101-2020_10_26_351783	65	8	NA	NA	NNP
10_1101-2020_10_26_351783	65	9	)	)	-RRB-
10_1101-2020_10_26_351783	65	10	probe	probe	NN
10_1101-2020_10_26_351783	65	11	P	p	NN
10_1101-2020_10_26_351783	65	12	-	-	HYPH
10_1101-2020_10_26_351783	65	13	values	value	NNS
10_1101-2020_10_26_351783	65	14	over	over	IN
10_1101-2020_10_26_351783	65	15	0.1	0.1	CD
10_1101-2020_10_26_351783	65	16	were	be	VBD
10_1101-2020_10_26_351783	65	17	also	also	RB
10_1101-2020_10_26_351783	65	18	removed	remove	VBN
10_1101-2020_10_26_351783	65	19	from	from	IN
10_1101-2020_10_26_351783	65	20	the	the	DT
10_1101-2020_10_26_351783	65	21	analysis	analysis	NN
10_1101-2020_10_26_351783	65	22	.	.	.
10_1101-2020_10_26_351783	66	1	Post	post	JJ
10_1101-2020_10_26_351783	66	2	-	-	JJ
10_1101-2020_10_26_351783	66	3	filtering	filtering	JJ
10_1101-2020_10_26_351783	66	4	imputation	imputation	NN
10_1101-2020_10_26_351783	66	5	of	of	IN
10_1101-2020_10_26_351783	66	6	NA	NA	NNP
10_1101-2020_10_26_351783	66	7	values	value	NNS
10_1101-2020_10_26_351783	66	8	was	be	VBD
10_1101-2020_10_26_351783	66	9	conducted	conduct	VBN
10_1101-2020_10_26_351783	66	10	on	on	IN
10_1101-2020_10_26_351783	66	11	the	the	DT
10_1101-2020_10_26_351783	66	12	beta	beta	JJ
10_1101-2020_10_26_351783	66	13	matrices	matrix	NNS
10_1101-2020_10_26_351783	66	14	,	,	,
10_1101-2020_10_26_351783	66	15	with	with	IN
10_1101-2020_10_26_351783	66	16	default	default	NN
10_1101-2020_10_26_351783	66	17	parameters	parameter	NNS
10_1101-2020_10_26_351783	66	18	(	(	-LRB-
10_1101-2020_10_26_351783	66	19	“	"	``
10_1101-2020_10_26_351783	66	20	combine	combine	VB
10_1101-2020_10_26_351783	66	21	”	"	''
10_1101-2020_10_26_351783	66	22	method	method	NN
10_1101-2020_10_26_351783	66	23	,	,	,
10_1101-2020_10_26_351783	66	24	k	k	NNP
10_1101-2020_10_26_351783	66	25	=	=	SYM
10_1101-2020_10_26_351783	66	26	5	5	CD
10_1101-2020_10_26_351783	66	27	,	,	,
10_1101-2020_10_26_351783	66	28	probe	probe	NNP
10_1101-2020_10_26_351783	66	29	cutoff	cutoff	NNP
10_1101-2020_10_26_351783	66	30	=	=	SYM
10_1101-2020_10_26_351783	66	31	0.2	0.2	CD
10_1101-2020_10_26_351783	66	32	,	,	,
10_1101-2020_10_26_351783	66	33	sample	sample	NN
10_1101-2020_10_26_351783	66	34	cutoff	cutoff	FW
10_1101-2020_10_26_351783	66	35	=	=	SYM
10_1101-2020_10_26_351783	66	36	0.1	0.1	CD
10_1101-2020_10_26_351783	66	37	)	)	-RRB-
10_1101-2020_10_26_351783	66	38	.	.	.
10_1101-2020_10_26_351783	67	1	Filtered	filter	VBN
10_1101-2020_10_26_351783	67	2	imputed	imputed	JJ
10_1101-2020_10_26_351783	67	3	matrices	matrix	NNS
10_1101-2020_10_26_351783	67	4	were	be	VBD
10_1101-2020_10_26_351783	67	5	normalized	normalized	JJ
10_1101-2020_10_26_351783	67	6	applying	apply	VBG
10_1101-2020_10_26_351783	67	7	the	the	DT
10_1101-2020_10_26_351783	67	8	Beta-	Beta-	NNP
10_1101-2020_10_26_351783	67	9	Mixture	Mixture	NNP
10_1101-2020_10_26_351783	67	10	Quantile	Quantile	NNP
10_1101-2020_10_26_351783	67	11	dilation	dilation	NN
10_1101-2020_10_26_351783	67	12	(	(	-LRB-
10_1101-2020_10_26_351783	67	13	BMIQ	BMIQ	NNP
10_1101-2020_10_26_351783	67	14	)	)	-RRB-
10_1101-2020_10_26_351783	67	15	normalization	normalization	NN
10_1101-2020_10_26_351783	67	16	method[15	method[15	NNP
10_1101-2020_10_26_351783	67	17	]	]	-RRB-
10_1101-2020_10_26_351783	67	18	�	�	NNP
10_1101-2020_10_26_351783	67	19	,	,	,
10_1101-2020_10_26_351783	67	20	including	include	VBG
10_1101-2020_10_26_351783	67	21	correction	correction	NN
10_1101-2020_10_26_351783	67	22	of	of	IN
10_1101-2020_10_26_351783	67	23	Type	type	NN
10_1101-2020_10_26_351783	67	24	-	-	HYPH
10_1101-2020_10_26_351783	67	25	I	I	NNP
10_1101-2020_10_26_351783	67	26	and	and	CC
10_1101-2020_10_26_351783	67	27	Type	Type	NNP
10_1101-2020_10_26_351783	67	28	-	-	HYPH
10_1101-2020_10_26_351783	67	29	II	II	NNP
10_1101-2020_10_26_351783	67	30	probe	probe	NN
10_1101-2020_10_26_351783	67	31	effects	effect	NNS
10_1101-2020_10_26_351783	67	32	.	.	.
10_1101-2020_10_26_351783	68	1	Data	datum	NNS
10_1101-2020_10_26_351783	68	2	quality	quality	NN
10_1101-2020_10_26_351783	68	3	was	be	VBD
10_1101-2020_10_26_351783	68	4	assessed	assess	VBN
10_1101-2020_10_26_351783	68	5	by	by	IN
10_1101-2020_10_26_351783	68	6	producing	produce	VBG
10_1101-2020_10_26_351783	68	7	multi	multi	JJ
10_1101-2020_10_26_351783	68	8	-	-	JJ
10_1101-2020_10_26_351783	68	9	dimensional	dimensional	JJ
10_1101-2020_10_26_351783	68	10	scaling	scaling	NN
10_1101-2020_10_26_351783	68	11	(	(	-LRB-
10_1101-2020_10_26_351783	68	12	MDS	MDS	NNP
10_1101-2020_10_26_351783	68	13	)	)	-RRB-
10_1101-2020_10_26_351783	68	14	plots	plot	NNS
10_1101-2020_10_26_351783	68	15	of	of	IN
10_1101-2020_10_26_351783	68	16	the	the	DT
10_1101-2020_10_26_351783	68	17	top	top	JJ
10_1101-2020_10_26_351783	68	18	1,000	1,000	CD
10_1101-2020_10_26_351783	68	19	most	most	RBS
10_1101-2020_10_26_351783	68	20	variable	variable	JJ
10_1101-2020_10_26_351783	68	21	positions	position	NNS
10_1101-2020_10_26_351783	68	22	per	per	IN
10_1101-2020_10_26_351783	68	23	sample	sample	NN
10_1101-2020_10_26_351783	68	24	,	,	,
10_1101-2020_10_26_351783	68	25	density	density	NN
10_1101-2020_10_26_351783	68	26	plots	plot	NNS
10_1101-2020_10_26_351783	68	27	for	for	IN
10_1101-2020_10_26_351783	68	28	the	the	DT
10_1101-2020_10_26_351783	68	29	distribution	distribution	NN
10_1101-2020_10_26_351783	68	30	of	of	IN
10_1101-2020_10_26_351783	68	31	beta	beta	JJ
10_1101-2020_10_26_351783	68	32	values	value	NNS
10_1101-2020_10_26_351783	68	33	,	,	,
10_1101-2020_10_26_351783	68	34	and	and	CC
10_1101-2020_10_26_351783	68	35	hierarchical	hierarchical	NN
10_1101-2020_10_26_351783	68	36	clustering	clustering	NN
10_1101-2020_10_26_351783	68	37	of	of	IN
10_1101-2020_10_26_351783	68	38	samples	sample	NNS
10_1101-2020_10_26_351783	68	39	,	,	,
10_1101-2020_10_26_351783	68	40	before	before	RB
10_1101-2020_10_26_351783	68	41	and	and	CC
10_1101-2020_10_26_351783	68	42	after	after	IN
10_1101-2020_10_26_351783	68	43	normalization	normalization	NN
10_1101-2020_10_26_351783	68	44	.	.	.
10_1101-2020_10_26_351783	69	1	Singular	singular	JJ
10_1101-2020_10_26_351783	69	2	value	value	NN
10_1101-2020_10_26_351783	69	3	decomposition	decomposition	NN
10_1101-2020_10_26_351783	69	4	(	(	-LRB-
10_1101-2020_10_26_351783	69	5	SVD	SVD	NNP
10_1101-2020_10_26_351783	69	6	)	)	-RRB-
10_1101-2020_10_26_351783	69	7	was	be	VBD
10_1101-2020_10_26_351783	69	8	used	use	VBN
10_1101-2020_10_26_351783	69	9	to	to	TO
10_1101-2020_10_26_351783	69	10	detect	detect	VB
10_1101-2020_10_26_351783	69	11	the	the	DT
10_1101-2020_10_26_351783	69	12	most	most	RBS
10_1101-2020_10_26_351783	69	13	significant	significant	JJ
10_1101-2020_10_26_351783	69	14	components	component	NNS
10_1101-2020_10_26_351783	69	15	of	of	IN
10_1101-2020_10_26_351783	69	16	variation	variation	NN
10_1101-2020_10_26_351783	69	17	in	in	IN
10_1101-2020_10_26_351783	69	18	the	the	DT
10_1101-2020_10_26_351783	69	19	data	datum	NNS
10_1101-2020_10_26_351783	69	20	.	.	.
10_1101-2020_10_26_351783	70	1	Unwanted	unwanted	JJ
10_1101-2020_10_26_351783	70	2	sources	source	NNS
10_1101-2020_10_26_351783	70	3	of	of	IN
10_1101-2020_10_26_351783	70	4	variation	variation	NN
10_1101-2020_10_26_351783	70	5	in	in	IN
10_1101-2020_10_26_351783	70	6	the	the	DT
10_1101-2020_10_26_351783	70	7	normalized	normalized	JJ
10_1101-2020_10_26_351783	70	8	data	datum	NNS
10_1101-2020_10_26_351783	70	9	were	be	VBD
10_1101-2020_10_26_351783	70	10	corrected	correct	VBN
10_1101-2020_10_26_351783	70	11	using	use	VBG
10_1101-2020_10_26_351783	70	12	ComBat	ComBat	NNP
10_1101-2020_10_26_351783	70	13	batch	batch	NN
10_1101-2020_10_26_351783	70	14	effect	effect	NN
10_1101-2020_10_26_351783	70	15	correction[16	correction[16	NNP
10_1101-2020_10_26_351783	70	16	]	]	-RRB-
10_1101-2020_10_26_351783	70	17	.	.	.
10_1101-2020_10_26_351783	71	1	Module	module	JJ
10_1101-2020_10_26_351783	71	2	Identification	identification	NN
10_1101-2020_10_26_351783	71	3	The	the	DT
10_1101-2020_10_26_351783	71	4	MODifieR13	modifier13	NN
10_1101-2020_10_26_351783	71	5	R	r	NN
10_1101-2020_10_26_351783	71	6	package	package	NN
10_1101-2020_10_26_351783	71	7	offers	offer	VBZ
10_1101-2020_10_26_351783	71	8	nine	nine	CD
10_1101-2020_10_26_351783	71	9	different	different	JJ
10_1101-2020_10_26_351783	71	10	methods	method	NNS
10_1101-2020_10_26_351783	71	11	for	for	IN
10_1101-2020_10_26_351783	71	12	producing	produce	VBG
10_1101-2020_10_26_351783	71	13	disease	disease	NN
10_1101-2020_10_26_351783	71	14	modules	module	NNS
10_1101-2020_10_26_351783	71	15	for	for	IN
10_1101-2020_10_26_351783	71	16	which	which	WDT
10_1101-2020_10_26_351783	71	17	we	-PRON-	PRP
10_1101-2020_10_26_351783	71	18	included	include	VBD
10_1101-2020_10_26_351783	71	19	all	all	DT
10_1101-2020_10_26_351783	71	20	but	but	CC
10_1101-2020_10_26_351783	71	21	Clique	Clique	NNP
10_1101-2020_10_26_351783	71	22	SuM	sum	NN
10_1101-2020_10_26_351783	71	23	exact	exact	JJ
10_1101-2020_10_26_351783	71	24	as	as	IN
10_1101-2020_10_26_351783	71	25	it	-PRON-	PRP
10_1101-2020_10_26_351783	71	26	is	be	VBZ
10_1101-2020_10_26_351783	71	27	highly	highly	RB
10_1101-2020_10_26_351783	71	28	similar	similar	JJ
10_1101-2020_10_26_351783	71	29	to	to	IN
10_1101-2020_10_26_351783	71	30	Clique	Clique	NNP
10_1101-2020_10_26_351783	71	31	SuM.	SuM.	NNP
10_1101-2020_10_26_351783	72	1	The	the	DT
10_1101-2020_10_26_351783	72	2	included	include	VBN
10_1101-2020_10_26_351783	72	3	methods	method	NNS
10_1101-2020_10_26_351783	72	4	will	will	MD
10_1101-2020_10_26_351783	72	5	produce	produce	VB
10_1101-2020_10_26_351783	72	6	modules	module	NNS
10_1101-2020_10_26_351783	72	7	based	base	VBN
10_1101-2020_10_26_351783	72	8	on	on	IN
10_1101-2020_10_26_351783	72	9	the	the	DT
10_1101-2020_10_26_351783	72	10	provided	provide	VBN
10_1101-2020_10_26_351783	72	11	omics	omic	NNS
10_1101-2020_10_26_351783	72	12	input	input	NN
10_1101-2020_10_26_351783	72	13	and	and	CC
10_1101-2020_10_26_351783	72	14	background	background	NN
10_1101-2020_10_26_351783	72	15	network	network	NN
10_1101-2020_10_26_351783	72	16	and	and	CC
10_1101-2020_10_26_351783	72	17	do	do	VBP
10_1101-2020_10_26_351783	72	18	not	not	RB
10_1101-2020_10_26_351783	72	19	include	include	VB
10_1101-2020_10_26_351783	72	20	prioritization	prioritization	NN
10_1101-2020_10_26_351783	72	21	of	of	IN
10_1101-2020_10_26_351783	72	22	pathway	pathway	NNP
10_1101-2020_10_26_351783	72	23	association	association	NNP
10_1101-2020_10_26_351783	72	24	.	.	.
10_1101-2020_10_26_351783	73	1	MODifieR	MODifieR	NNP
10_1101-2020_10_26_351783	73	2	methods	method	NNS
10_1101-2020_10_26_351783	73	3	used	use	VBN
10_1101-2020_10_26_351783	73	4	for	for	IN
10_1101-2020_10_26_351783	73	5	module	module	JJ
10_1101-2020_10_26_351783	73	6	identification	identification	NN
10_1101-2020_10_26_351783	73	7	through	through	IN
10_1101-2020_10_26_351783	73	8	this	this	DT
10_1101-2020_10_26_351783	73	9	study	study	NN
10_1101-2020_10_26_351783	73	10	are	be	VBP
10_1101-2020_10_26_351783	73	11	listed	list	VBN
10_1101-2020_10_26_351783	73	12	in	in	IN
10_1101-2020_10_26_351783	73	13	the	the	DT
10_1101-2020_10_26_351783	73	14	Supplementary	Supplementary	NNP
10_1101-2020_10_26_351783	73	15	Table	Table	NNP
10_1101-2020_10_26_351783	73	16	3	3	CD
10_1101-2020_10_26_351783	73	17	.	.	.
10_1101-2020_10_26_351783	74	1	For	for	IN
10_1101-2020_10_26_351783	74	2	the	the	DT
10_1101-2020_10_26_351783	74	3	methods	method	NNS
10_1101-2020_10_26_351783	74	4	that	that	WDT
10_1101-2020_10_26_351783	74	5	require	require	VBP
10_1101-2020_10_26_351783	74	6	a	a	DT
10_1101-2020_10_26_351783	74	7	network	network	NN
10_1101-2020_10_26_351783	74	8	,	,	,
10_1101-2020_10_26_351783	74	9	we	-PRON-	PRP
10_1101-2020_10_26_351783	74	10	used	use	VBD
10_1101-2020_10_26_351783	74	11	the	the	DT
10_1101-2020_10_26_351783	74	12	(	(	-LRB-
10_1101-2020_10_26_351783	74	13	which	which	WDT
10_1101-2020_10_26_351783	74	14	was	be	VBD
10_1101-2020_10_26_351783	74	15	not	not	RB
10_1101-2020_10_26_351783	74	16	certified	certify	VBN
10_1101-2020_10_26_351783	74	17	by	by	IN
10_1101-2020_10_26_351783	74	18	peer	peer	NN
10_1101-2020_10_26_351783	74	19	review	review	NN
10_1101-2020_10_26_351783	74	20	)	)	-RRB-
10_1101-2020_10_26_351783	74	21	is	be	VBZ
10_1101-2020_10_26_351783	74	22	the	the	DT
10_1101-2020_10_26_351783	74	23	author	author	NN
10_1101-2020_10_26_351783	74	24	/	/	SYM
10_1101-2020_10_26_351783	74	25	funder	funder	NN
10_1101-2020_10_26_351783	74	26	.	.	.
10_1101-2020_10_26_351783	75	1	All	all	DT
10_1101-2020_10_26_351783	75	2	rights	right	NNS
10_1101-2020_10_26_351783	75	3	reserved	reserve	VBD
10_1101-2020_10_26_351783	75	4	.	.	.
10_1101-2020_10_26_351783	76	1	No	no	DT
10_1101-2020_10_26_351783	76	2	reuse	reuse	NN
10_1101-2020_10_26_351783	76	3	allowed	allow	VBN
10_1101-2020_10_26_351783	76	4	without	without	IN
10_1101-2020_10_26_351783	76	5	permission	permission	NN
10_1101-2020_10_26_351783	76	6	.	.	.
10_1101-2020_10_26_351783	77	1	The	the	DT
10_1101-2020_10_26_351783	77	2	copyright	copyright	NN
10_1101-2020_10_26_351783	77	3	holder	holder	NN
10_1101-2020_10_26_351783	77	4	for	for	IN
10_1101-2020_10_26_351783	77	5	this	this	DT
10_1101-2020_10_26_351783	77	6	preprintthis	preprintthis	NN
10_1101-2020_10_26_351783	77	7	version	version	NN
10_1101-2020_10_26_351783	77	8	posted	post	VBD
10_1101-2020_10_26_351783	77	9	January	January	NNP
10_1101-2020_10_26_351783	77	10	6	6	CD
10_1101-2020_10_26_351783	77	11	,	,	,
10_1101-2020_10_26_351783	77	12	2021	2021	CD
10_1101-2020_10_26_351783	77	13	.	.	.
10_1101-2020_10_26_351783	77	14	;	;	:
10_1101-2020_10_26_351783	77	15	https://doi.org/10.1101/2020.10.26.351783doi	https://doi.org/10.1101/2020.10.26.351783doi	ADD
10_1101-2020_10_26_351783	77	16	:	:	:
10_1101-2020_10_26_351783	77	17	bioRxiv	biorxiv	IN
10_1101-2020_10_26_351783	77	18	preprint	preprint	NN
10_1101-2020_10_26_351783	77	19	https://doi.org/10.1101/2020.10.26.351783	https://doi.org/10.1101/2020.10.26.351783	NNP
10_1101-2020_10_26_351783	77	20	7	7	CD
10_1101-2020_10_26_351783	77	21	human	human	JJ
10_1101-2020_10_26_351783	77	22	PPI	PPI	NNP
10_1101-2020_10_26_351783	77	23	network	network	NN
10_1101-2020_10_26_351783	77	24	from	from	IN
10_1101-2020_10_26_351783	77	25	STRING5	STRING5	NNP
10_1101-2020_10_26_351783	77	26	database	database	NNP
10_1101-2020_10_26_351783	77	27	version	version	NNP
10_1101-2020_10_26_351783	77	28	11	11	CD
10_1101-2020_10_26_351783	77	29	,	,	,
10_1101-2020_10_26_351783	77	30	consisting	consist	VBG
10_1101-2020_10_26_351783	77	31	of	of	IN
10_1101-2020_10_26_351783	77	32	11,295,036	11,295,036	CD
10_1101-2020_10_26_351783	77	33	interactions	interaction	NNS
10_1101-2020_10_26_351783	77	34	among	among	IN
10_1101-2020_10_26_351783	77	35	18,746	18,746	CD
10_1101-2020_10_26_351783	77	36	unique	unique	JJ
10_1101-2020_10_26_351783	77	37	genes	gene	NNS
10_1101-2020_10_26_351783	77	38	/	/	SYM
10_1101-2020_10_26_351783	77	39	proteins	protein	NNS
10_1101-2020_10_26_351783	77	40	.	.	.
10_1101-2020_10_26_351783	78	1	We	-PRON-	PRP
10_1101-2020_10_26_351783	78	2	filtered	filter	VBD
10_1101-2020_10_26_351783	78	3	the	the	DT
10_1101-2020_10_26_351783	78	4	network	network	NN
10_1101-2020_10_26_351783	78	5	to	to	TO
10_1101-2020_10_26_351783	78	6	have	have	VB
10_1101-2020_10_26_351783	78	7	high	high	JJ
10_1101-2020_10_26_351783	78	8	confidence	confidence	NN
10_1101-2020_10_26_351783	78	9	interactions	interaction	NNS
10_1101-2020_10_26_351783	78	10	by	by	IN
10_1101-2020_10_26_351783	78	11	using	use	VBG
10_1101-2020_10_26_351783	78	12	the	the	DT
10_1101-2020_10_26_351783	78	13	cutoff	cutoff	NN
10_1101-2020_10_26_351783	78	14	>	>	XX
10_1101-2020_10_26_351783	78	15	900	900	CD
10_1101-2020_10_26_351783	78	16	to	to	TO
10_1101-2020_10_26_351783	78	17	reduce	reduce	VB
10_1101-2020_10_26_351783	78	18	the	the	DT
10_1101-2020_10_26_351783	78	19	number	number	NN
10_1101-2020_10_26_351783	78	20	of	of	IN
10_1101-2020_10_26_351783	78	21	false	false	JJ
10_1101-2020_10_26_351783	78	22	positives	positive	NNS
10_1101-2020_10_26_351783	78	23	,	,	,
10_1101-2020_10_26_351783	78	24	resulting	result	VBG
10_1101-2020_10_26_351783	78	25	in	in	IN
10_1101-2020_10_26_351783	78	26	a	a	DT
10_1101-2020_10_26_351783	78	27	subset	subset	NN
10_1101-2020_10_26_351783	78	28	of	of	IN
10_1101-2020_10_26_351783	78	29	631,782	631,782	CD
10_1101-2020_10_26_351783	78	30	interactions	interaction	NNS
10_1101-2020_10_26_351783	78	31	between	between	IN
10_1101-2020_10_26_351783	78	32	12,123	12,123	CD
10_1101-2020_10_26_351783	78	33	unique	unique	JJ
10_1101-2020_10_26_351783	78	34	genes	gene	NNS
10_1101-2020_10_26_351783	78	35	/	/	SYM
10_1101-2020_10_26_351783	78	36	proteins	protein	NNS
10_1101-2020_10_26_351783	78	37	.	.	.
10_1101-2020_10_26_351783	79	1	For	for	IN
10_1101-2020_10_26_351783	79	2	co	co	JJ
10_1101-2020_10_26_351783	79	3	-	-	JJ
10_1101-2020_10_26_351783	79	4	expression	expression	JJ
10_1101-2020_10_26_351783	79	5	methods	method	NNS
10_1101-2020_10_26_351783	79	6	,	,	,
10_1101-2020_10_26_351783	79	7	the	the	DT
10_1101-2020_10_26_351783	79	8	network	network	NN
10_1101-2020_10_26_351783	79	9	is	be	VBZ
10_1101-2020_10_26_351783	79	10	computed	compute	VBN
10_1101-2020_10_26_351783	79	11	within	within	IN
10_1101-2020_10_26_351783	79	12	the	the	DT
10_1101-2020_10_26_351783	79	13	method	method	NN
10_1101-2020_10_26_351783	79	14	algorithm	algorithm	NN
10_1101-2020_10_26_351783	79	15	from	from	IN
10_1101-2020_10_26_351783	79	16	the	the	DT
10_1101-2020_10_26_351783	79	17	gene	gene	NN
10_1101-2020_10_26_351783	79	18	expression	expression	NN
10_1101-2020_10_26_351783	79	19	matrix	matrix	NNP
10_1101-2020_10_26_351783	79	20	.	.	.
10_1101-2020_10_26_351783	80	1	In	in	IN
10_1101-2020_10_26_351783	80	2	case	case	NN
10_1101-2020_10_26_351783	80	3	of	of	IN
10_1101-2020_10_26_351783	80	4	the	the	DT
10_1101-2020_10_26_351783	80	5	benchmark	benchmark	JJ
10_1101-2020_10_26_351783	80	6	analysis	analysis	NN
10_1101-2020_10_26_351783	80	7	,	,	,
10_1101-2020_10_26_351783	80	8	we	-PRON-	PRP
10_1101-2020_10_26_351783	80	9	used	use	VBD
10_1101-2020_10_26_351783	80	10	a	a	DT
10_1101-2020_10_26_351783	80	11	stringent	stringent	JJ
10_1101-2020_10_26_351783	80	12	cutoff	cutoff	NN
10_1101-2020_10_26_351783	80	13	of	of	IN
10_1101-2020_10_26_351783	80	14	score	score	NN
10_1101-2020_10_26_351783	80	15	>	>	XX
10_1101-2020_10_26_351783	80	16	900	900	CD
10_1101-2020_10_26_351783	80	17	,	,	,
10_1101-2020_10_26_351783	80	18	so	so	IN
10_1101-2020_10_26_351783	80	19	that	that	IN
10_1101-2020_10_26_351783	80	20	the	the	DT
10_1101-2020_10_26_351783	80	21	runs	run	NNS
10_1101-2020_10_26_351783	80	22	were	be	VBD
10_1101-2020_10_26_351783	80	23	not	not	RB
10_1101-2020_10_26_351783	80	24	computationally	computationally	RB
10_1101-2020_10_26_351783	80	25	intensive	intensive	JJ
10_1101-2020_10_26_351783	80	26	.	.	.
10_1101-2020_10_26_351783	81	1	For	for	IN
10_1101-2020_10_26_351783	81	2	the	the	DT
10_1101-2020_10_26_351783	81	3	MS	MS	NNP
10_1101-2020_10_26_351783	81	4	use	use	NN
10_1101-2020_10_26_351783	81	5	case	case	NN
10_1101-2020_10_26_351783	81	6	benchmark	benchmark	NN
10_1101-2020_10_26_351783	81	7	,	,	,
10_1101-2020_10_26_351783	81	8	we	-PRON-	PRP
10_1101-2020_10_26_351783	81	9	used	use	VBD
10_1101-2020_10_26_351783	81	10	the	the	DT
10_1101-2020_10_26_351783	81	11	network	network	NN
10_1101-2020_10_26_351783	81	12	combined	combine	VBN
10_1101-2020_10_26_351783	81	13	score	score	NN
10_1101-2020_10_26_351783	81	14	cutoff	cutoff	RB
10_1101-2020_10_26_351783	81	15	>	>	XX
10_1101-2020_10_26_351783	81	16	700	700	CD
10_1101-2020_10_26_351783	81	17	.	.	.
10_1101-2020_10_26_351783	82	1	The	the	DT
10_1101-2020_10_26_351783	82	2	processed	processed	JJ
10_1101-2020_10_26_351783	82	3	matrix	matrix	NN
10_1101-2020_10_26_351783	82	4	for	for	IN
10_1101-2020_10_26_351783	82	5	each	each	DT
10_1101-2020_10_26_351783	82	6	dataset	dataset	NN
10_1101-2020_10_26_351783	82	7	and	and	CC
10_1101-2020_10_26_351783	82	8	their	-PRON-	PRP$
10_1101-2020_10_26_351783	82	9	respective	respective	JJ
10_1101-2020_10_26_351783	82	10	phenotypic	phenotypic	JJ
10_1101-2020_10_26_351783	82	11	information	information	NN
10_1101-2020_10_26_351783	82	12	were	be	VBD
10_1101-2020_10_26_351783	82	13	downloaded	download	VBN
10_1101-2020_10_26_351783	82	14	from	from	IN
10_1101-2020_10_26_351783	82	15	GEO	GEO	NNP
10_1101-2020_10_26_351783	82	16	.	.	.
10_1101-2020_10_26_351783	83	1	The	the	DT
10_1101-2020_10_26_351783	83	2	input	input	NN
10_1101-2020_10_26_351783	83	3	object	object	NN
10_1101-2020_10_26_351783	83	4	is	be	VBZ
10_1101-2020_10_26_351783	83	5	prepared	prepare	VBN
10_1101-2020_10_26_351783	83	6	using	use	VBG
10_1101-2020_10_26_351783	83	7	the	the	DT
10_1101-2020_10_26_351783	83	8	create_input_microarray	create_input_microarray	NN
10_1101-2020_10_26_351783	83	9	function	function	NN
10_1101-2020_10_26_351783	83	10	from	from	IN
10_1101-2020_10_26_351783	83	11	the	the	DT
10_1101-2020_10_26_351783	83	12	MODifieR	MODifieR	NNP
10_1101-2020_10_26_351783	83	13	package	package	NN
10_1101-2020_10_26_351783	83	14	which	which	WDT
10_1101-2020_10_26_351783	83	15	is	be	VBZ
10_1101-2020_10_26_351783	83	16	then	then	RB
10_1101-2020_10_26_351783	83	17	used	use	VBN
10_1101-2020_10_26_351783	83	18	for	for	IN
10_1101-2020_10_26_351783	83	19	creating	create	VBG
10_1101-2020_10_26_351783	83	20	the	the	DT
10_1101-2020_10_26_351783	83	21	modules	module	NNS
10_1101-2020_10_26_351783	83	22	.	.	.
10_1101-2020_10_26_351783	84	1	The	the	DT
10_1101-2020_10_26_351783	84	2	input	input	NN
10_1101-2020_10_26_351783	84	3	function	function	NN
10_1101-2020_10_26_351783	84	4	applies	apply	VBZ
10_1101-2020_10_26_351783	84	5	linear	linear	JJ
10_1101-2020_10_26_351783	84	6	model	model	NN
10_1101-2020_10_26_351783	84	7	using	use	VBG
10_1101-2020_10_26_351783	84	8	limma	limma	NN
10_1101-2020_10_26_351783	84	9	for	for	IN
10_1101-2020_10_26_351783	84	10	comparison	comparison	NN
10_1101-2020_10_26_351783	84	11	of	of	IN
10_1101-2020_10_26_351783	84	12	patient	patient	NNP
10_1101-2020_10_26_351783	84	13	's	's	POS
10_1101-2020_10_26_351783	84	14	vs	vs	NNP
10_1101-2020_10_26_351783	84	15	controls	control	NNS
10_1101-2020_10_26_351783	84	16	to	to	TO
10_1101-2020_10_26_351783	84	17	get	get	VB
10_1101-2020_10_26_351783	84	18	the	the	DT
10_1101-2020_10_26_351783	84	19	differentially	differentially	RB
10_1101-2020_10_26_351783	84	20	methylated	methylate	VBN
10_1101-2020_10_26_351783	84	21	or	or	CC
10_1101-2020_10_26_351783	84	22	expressed	express	VBN
10_1101-2020_10_26_351783	84	23	genes	gene	NNS
10_1101-2020_10_26_351783	84	24	.	.	.
10_1101-2020_10_26_351783	85	1	A	a	DT
10_1101-2020_10_26_351783	85	2	dynamic	dynamic	JJ
10_1101-2020_10_26_351783	85	3	cutoff	cutoff	NN
10_1101-2020_10_26_351783	85	4	of	of	IN
10_1101-2020_10_26_351783	85	5	5	5	CD
10_1101-2020_10_26_351783	85	6	%	%	NN
10_1101-2020_10_26_351783	85	7	in	in	IN
10_1101-2020_10_26_351783	85	8	the	the	DT
10_1101-2020_10_26_351783	85	9	differentially	differentially	RB
10_1101-2020_10_26_351783	85	10	methylated	methylate	VBN
10_1101-2020_10_26_351783	85	11	or	or	CC
10_1101-2020_10_26_351783	85	12	expressed	express	VBN
10_1101-2020_10_26_351783	85	13	genes	gene	NNS
10_1101-2020_10_26_351783	85	14	is	be	VBZ
10_1101-2020_10_26_351783	85	15	applied	apply	VBN
10_1101-2020_10_26_351783	85	16	for	for	IN
10_1101-2020_10_26_351783	85	17	input	input	NN
10_1101-2020_10_26_351783	85	18	seed	seed	NN
10_1101-2020_10_26_351783	85	19	genes	gene	NNS
10_1101-2020_10_26_351783	85	20	for	for	IN
10_1101-2020_10_26_351783	85	21	the	the	DT
10_1101-2020_10_26_351783	85	22	methods	method	NNS
10_1101-2020_10_26_351783	85	23	that	that	WDT
10_1101-2020_10_26_351783	85	24	require	require	VBP
10_1101-2020_10_26_351783	85	25	seed	seed	NN
10_1101-2020_10_26_351783	85	26	genes	gene	NNS
10_1101-2020_10_26_351783	85	27	.	.	.
10_1101-2020_10_26_351783	86	1	Differential	differential	NN
10_1101-2020_10_26_351783	86	2	methylation	methylation	NN
10_1101-2020_10_26_351783	86	3	analysis	analysis	NN
10_1101-2020_10_26_351783	86	4	of	of	IN
10_1101-2020_10_26_351783	86	5	risk	risk	NN
10_1101-2020_10_26_351783	86	6	factor	factor	NN
10_1101-2020_10_26_351783	86	7	data	datum	NNS
10_1101-2020_10_26_351783	86	8	Differentially	differentially	RB
10_1101-2020_10_26_351783	86	9	methylated	methylate	VBD
10_1101-2020_10_26_351783	86	10	probes	probe	NNS
10_1101-2020_10_26_351783	86	11	(	(	-LRB-
10_1101-2020_10_26_351783	86	12	DMPs	DMPs	NNP
10_1101-2020_10_26_351783	86	13	)	)	-RRB-
10_1101-2020_10_26_351783	86	14	were	be	VBD
10_1101-2020_10_26_351783	86	15	found	find	VBN
10_1101-2020_10_26_351783	86	16	by	by	IN
10_1101-2020_10_26_351783	86	17	fitting	fit	VBG
10_1101-2020_10_26_351783	86	18	a	a	DT
10_1101-2020_10_26_351783	86	19	linear	linear	JJ
10_1101-2020_10_26_351783	86	20	model	model	NN
10_1101-2020_10_26_351783	86	21	to	to	IN
10_1101-2020_10_26_351783	86	22	the	the	DT
10_1101-2020_10_26_351783	86	23	data	datum	NNS
10_1101-2020_10_26_351783	86	24	using	use	VBG
10_1101-2020_10_26_351783	86	25	the	the	DT
10_1101-2020_10_26_351783	86	26	limma	limma	NN
10_1101-2020_10_26_351783	86	27	R	R	NNP
10_1101-2020_10_26_351783	86	28	package[17	package[17	NNP
10_1101-2020_10_26_351783	86	29	]	]	-RRB-
10_1101-2020_10_26_351783	86	30	�	�	NNP
10_1101-2020_10_26_351783	86	31	,	,	,
10_1101-2020_10_26_351783	86	32	version	version	NN
10_1101-2020_10_26_351783	86	33	3.42.2	3.42.2	CD
10_1101-2020_10_26_351783	86	34	implemented	implement	VBN
10_1101-2020_10_26_351783	86	35	in	in	IN
10_1101-2020_10_26_351783	86	36	the	the	DT
10_1101-2020_10_26_351783	86	37	ChAMP	ChAMP	NNP
10_1101-2020_10_26_351783	86	38	function	function	NN
10_1101-2020_10_26_351783	86	39	champ	champ	NN
10_1101-2020_10_26_351783	86	40	.	.	.
10_1101-2020_10_26_351783	86	41	DMP	DMP	NNP
10_1101-2020_10_26_351783	86	42	.	.	.
10_1101-2020_10_26_351783	87	1	P	p	NN
10_1101-2020_10_26_351783	87	2	-	-	HYPH
10_1101-2020_10_26_351783	87	3	values	value	NNS
10_1101-2020_10_26_351783	87	4	were	be	VBD
10_1101-2020_10_26_351783	87	5	adjusted	adjust	VBN
10_1101-2020_10_26_351783	87	6	for	for	IN
10_1101-2020_10_26_351783	87	7	multiple	multiple	JJ
10_1101-2020_10_26_351783	87	8	testing	testing	NN
10_1101-2020_10_26_351783	87	9	using	use	VBG
10_1101-2020_10_26_351783	87	10	Benjamini	Benjamini	NNP
10_1101-2020_10_26_351783	87	11	-	-	HYPH
10_1101-2020_10_26_351783	87	12	Hochberg	Hochberg	NNP
10_1101-2020_10_26_351783	87	13	False	False	NNP
10_1101-2020_10_26_351783	87	14	Discovery	Discovery	NNP
10_1101-2020_10_26_351783	87	15	Rate	Rate	NNP
10_1101-2020_10_26_351783	87	16	(	(	-LRB-
10_1101-2020_10_26_351783	87	17	FDR	FDR	NNP
10_1101-2020_10_26_351783	87	18	)	)	-RRB-
10_1101-2020_10_26_351783	87	19	correction	correction	NN
10_1101-2020_10_26_351783	87	20	.	.	.
10_1101-2020_10_26_351783	88	1	Differentially	differentially	RB
10_1101-2020_10_26_351783	88	2	methylated	methylate	VBN
10_1101-2020_10_26_351783	88	3	genes	gene	NNS
10_1101-2020_10_26_351783	88	4	(	(	-LRB-
10_1101-2020_10_26_351783	88	5	DMGs	DMGs	NNP
10_1101-2020_10_26_351783	88	6	)	)	-RRB-
10_1101-2020_10_26_351783	88	7	were	be	VBD
10_1101-2020_10_26_351783	88	8	obtained	obtain	VBN
10_1101-2020_10_26_351783	88	9	and	and	CC
10_1101-2020_10_26_351783	88	10	annotated	annotate	VBN
10_1101-2020_10_26_351783	88	11	using	use	VBG
10_1101-2020_10_26_351783	88	12	the	the	DT
10_1101-2020_10_26_351783	88	13	org.Hs.eg.db	org.Hs.eg.db	NNP
10_1101-2020_10_26_351783	88	14	R	r	NN
10_1101-2020_10_26_351783	88	15	package	package	NN
10_1101-2020_10_26_351783	88	16	�	�	NNP
10_1101-2020_10_26_351783	88	17	,	,	,
10_1101-2020_10_26_351783	88	18	version	version	NN
10_1101-2020_10_26_351783	88	19	3.10.0	3.10.0	CD
10_1101-2020_10_26_351783	88	20	.	.	.
10_1101-2020_10_26_351783	89	1	DMG	DMG	NNP
10_1101-2020_10_26_351783	89	2	lists	list	NNS
10_1101-2020_10_26_351783	89	3	were	be	VBD
10_1101-2020_10_26_351783	89	4	cross	cross	JJ
10_1101-2020_10_26_351783	89	5	-	-	VBN
10_1101-2020_10_26_351783	89	6	checked	check	VBN
10_1101-2020_10_26_351783	89	7	against	against	IN
10_1101-2020_10_26_351783	89	8	the	the	DT
10_1101-2020_10_26_351783	89	9	STRING	string	NN
10_1101-2020_10_26_351783	89	10	database	database	NN
10_1101-2020_10_26_351783	89	11	version	version	NN
10_1101-2020_10_26_351783	89	12	11	11	CD
10_1101-2020_10_26_351783	89	13	PPI	PPI	NNP
10_1101-2020_10_26_351783	89	14	network	network	NN
10_1101-2020_10_26_351783	89	15	used	use	VBN
10_1101-2020_10_26_351783	89	16	for	for	IN
10_1101-2020_10_26_351783	89	17	module	module	JJ
10_1101-2020_10_26_351783	89	18	identification	identification	NN
10_1101-2020_10_26_351783	89	19	in	in	IN
10_1101-2020_10_26_351783	89	20	the	the	DT
10_1101-2020_10_26_351783	89	21	MS	MS	NNP
10_1101-2020_10_26_351783	89	22	multi	multi	JJ
10_1101-2020_10_26_351783	89	23	-	-	HYPH
10_1101-2020_10_26_351783	89	24	omics	omics	JJ
10_1101-2020_10_26_351783	89	25	approach	approach	NN
10_1101-2020_10_26_351783	89	26	(	(	-LRB-
10_1101-2020_10_26_351783	89	27	high	high	JJ
10_1101-2020_10_26_351783	89	28	confidence	confidence	NN
10_1101-2020_10_26_351783	89	29	interactions	interaction	NNS
10_1101-2020_10_26_351783	89	30	,	,	,
10_1101-2020_10_26_351783	89	31	combined	combine	VBN
10_1101-2020_10_26_351783	89	32	score	score	NN
10_1101-2020_10_26_351783	89	33	>	>	XX
10_1101-2020_10_26_351783	89	34	700	700	CD
10_1101-2020_10_26_351783	89	35	)	)	-RRB-
10_1101-2020_10_26_351783	89	36	.	.	.
10_1101-2020_10_26_351783	90	1	DMGs	dmg	NNS
10_1101-2020_10_26_351783	90	2	that	that	WDT
10_1101-2020_10_26_351783	90	3	were	be	VBD
10_1101-2020_10_26_351783	90	4	not	not	RB
10_1101-2020_10_26_351783	90	5	present	present	JJ
10_1101-2020_10_26_351783	90	6	in	in	IN
10_1101-2020_10_26_351783	90	7	the	the	DT
10_1101-2020_10_26_351783	90	8	PPI	PPI	NNP
10_1101-2020_10_26_351783	90	9	network	network	NN
10_1101-2020_10_26_351783	90	10	were	be	VBD
10_1101-2020_10_26_351783	90	11	removed	remove	VBN
10_1101-2020_10_26_351783	90	12	.	.	.
10_1101-2020_10_26_351783	91	1	In	in	IN
10_1101-2020_10_26_351783	91	2	case	case	NN
10_1101-2020_10_26_351783	91	3	of	of	IN
10_1101-2020_10_26_351783	91	4	the	the	DT
10_1101-2020_10_26_351783	91	5	additional	additional	JJ
10_1101-2020_10_26_351783	91	6	MS	MS	NNP
10_1101-2020_10_26_351783	91	7	validation	validation	NN
10_1101-2020_10_26_351783	91	8	dataset	dataset	NN
10_1101-2020_10_26_351783	91	9	,	,	,
10_1101-2020_10_26_351783	91	10	a	a	DT
10_1101-2020_10_26_351783	91	11	linear	linear	JJ
10_1101-2020_10_26_351783	91	12	mixed	mixed	JJ
10_1101-2020_10_26_351783	91	13	effect	effect	NN
10_1101-2020_10_26_351783	91	14	model	model	NN
10_1101-2020_10_26_351783	91	15	with	with	IN
10_1101-2020_10_26_351783	91	16	risk	risk	NN
10_1101-2020_10_26_351783	91	17	factors	factor	NNS
10_1101-2020_10_26_351783	91	18	(	(	-LRB-
10_1101-2020_10_26_351783	91	19	age	age	NN
10_1101-2020_10_26_351783	91	20	,	,	,
10_1101-2020_10_26_351783	91	21	sex	sex	NN
10_1101-2020_10_26_351783	91	22	,	,	,
10_1101-2020_10_26_351783	91	23	BMI	BMI	NNP
10_1101-2020_10_26_351783	91	24	at	at	IN
10_1101-2020_10_26_351783	91	25	age	age	NN
10_1101-2020_10_26_351783	91	26	of	of	IN
10_1101-2020_10_26_351783	91	27	20	20	CD
10_1101-2020_10_26_351783	91	28	,	,	,
10_1101-2020_10_26_351783	91	29	smoking	smoking	NN
10_1101-2020_10_26_351783	91	30	,	,	,
10_1101-2020_10_26_351783	91	31	alcohol	alcohol	NN
10_1101-2020_10_26_351783	91	32	consumption	consumption	NN
10_1101-2020_10_26_351783	91	33	,	,	,
10_1101-2020_10_26_351783	91	34	sun	sun	NN
10_1101-2020_10_26_351783	91	35	exposure	exposure	NN
10_1101-2020_10_26_351783	91	36	,	,	,
10_1101-2020_10_26_351783	91	37	night	night	NN
10_1101-2020_10_26_351783	91	38	shift	shift	NN
10_1101-2020_10_26_351783	91	39	work	work	NN
10_1101-2020_10_26_351783	91	40	,	,	,
10_1101-2020_10_26_351783	91	41	contact	contact	NN
10_1101-2020_10_26_351783	91	42	with	with	IN
10_1101-2020_10_26_351783	91	43	organic	organic	JJ
10_1101-2020_10_26_351783	91	44	solvents	solvent	NNS
10_1101-2020_10_26_351783	91	45	)	)	-RRB-
10_1101-2020_10_26_351783	91	46	as	as	IN
10_1101-2020_10_26_351783	91	47	categorical	categorical	JJ
10_1101-2020_10_26_351783	91	48	covariates	covariate	NNS
10_1101-2020_10_26_351783	91	49	was	be	VBD
10_1101-2020_10_26_351783	91	50	implemented	implement	VBN
10_1101-2020_10_26_351783	91	51	to	to	TO
10_1101-2020_10_26_351783	91	52	find	find	VB
10_1101-2020_10_26_351783	91	53	the	the	DT
10_1101-2020_10_26_351783	91	54	differentially	differentially	RB
10_1101-2020_10_26_351783	91	55	methylated	methylate	VBN
10_1101-2020_10_26_351783	91	56	genes	gene	NNS
10_1101-2020_10_26_351783	91	57	after	after	IN
10_1101-2020_10_26_351783	91	58	the	the	DT
10_1101-2020_10_26_351783	91	59	preprocessing	preprocessing	NN
10_1101-2020_10_26_351783	91	60	step	step	NN
10_1101-2020_10_26_351783	91	61	,	,	,
10_1101-2020_10_26_351783	91	62	as	as	IN
10_1101-2020_10_26_351783	91	63	described	describe	VBN
10_1101-2020_10_26_351783	91	64	in	in	IN
10_1101-2020_10_26_351783	91	65	the	the	DT
10_1101-2020_10_26_351783	91	66	preprocessing	preprocessing	NN
10_1101-2020_10_26_351783	91	67	section	section	NN
10_1101-2020_10_26_351783	91	68	of	of	IN
10_1101-2020_10_26_351783	91	69	the	the	DT
10_1101-2020_10_26_351783	91	70	methods	method	NNS
10_1101-2020_10_26_351783	91	71	.	.	.
10_1101-2020_10_26_351783	92	1	Since	since	IN
10_1101-2020_10_26_351783	92	2	all	all	PDT
10_1101-2020_10_26_351783	92	3	the	the	DT
10_1101-2020_10_26_351783	92	4	patients	patient	NNS
10_1101-2020_10_26_351783	92	5	were	be	VBD
10_1101-2020_10_26_351783	92	6	EBV	EBV	NNP
10_1101-2020_10_26_351783	92	7	positive	positive	JJ
10_1101-2020_10_26_351783	92	8	,	,	,
10_1101-2020_10_26_351783	92	9	we	-PRON-	PRP
10_1101-2020_10_26_351783	92	10	did	do	VBD
10_1101-2020_10_26_351783	92	11	not	not	RB
10_1101-2020_10_26_351783	92	12	include	include	VB
10_1101-2020_10_26_351783	92	13	it	-PRON-	PRP
10_1101-2020_10_26_351783	92	14	for	for	IN
10_1101-2020_10_26_351783	92	15	linear	linear	JJ
10_1101-2020_10_26_351783	92	16	mixed	mixed	JJ
10_1101-2020_10_26_351783	92	17	effect	effect	NN
10_1101-2020_10_26_351783	92	18	model	model	NN
10_1101-2020_10_26_351783	92	19	.	.	.
10_1101-2020_10_26_351783	93	1	(	(	-LRB-
10_1101-2020_10_26_351783	93	2	which	which	WDT
10_1101-2020_10_26_351783	93	3	was	be	VBD
10_1101-2020_10_26_351783	93	4	not	not	RB
10_1101-2020_10_26_351783	93	5	certified	certify	VBN
10_1101-2020_10_26_351783	93	6	by	by	IN
10_1101-2020_10_26_351783	93	7	peer	peer	NN
10_1101-2020_10_26_351783	93	8	review	review	NN
10_1101-2020_10_26_351783	93	9	)	)	-RRB-
10_1101-2020_10_26_351783	93	10	is	be	VBZ
10_1101-2020_10_26_351783	93	11	the	the	DT
10_1101-2020_10_26_351783	93	12	author	author	NN
10_1101-2020_10_26_351783	93	13	/	/	SYM
10_1101-2020_10_26_351783	93	14	funder	funder	NN
10_1101-2020_10_26_351783	93	15	.	.	.
10_1101-2020_10_26_351783	94	1	All	all	DT
10_1101-2020_10_26_351783	94	2	rights	right	NNS
10_1101-2020_10_26_351783	94	3	reserved	reserve	VBD
10_1101-2020_10_26_351783	94	4	.	.	.
10_1101-2020_10_26_351783	95	1	No	no	DT
10_1101-2020_10_26_351783	95	2	reuse	reuse	NN
10_1101-2020_10_26_351783	95	3	allowed	allow	VBN
10_1101-2020_10_26_351783	95	4	without	without	IN
10_1101-2020_10_26_351783	95	5	permission	permission	NN
10_1101-2020_10_26_351783	95	6	.	.	.
10_1101-2020_10_26_351783	96	1	The	the	DT
10_1101-2020_10_26_351783	96	2	copyright	copyright	NN
10_1101-2020_10_26_351783	96	3	holder	holder	NN
10_1101-2020_10_26_351783	96	4	for	for	IN
10_1101-2020_10_26_351783	96	5	this	this	DT
10_1101-2020_10_26_351783	96	6	preprintthis	preprintthis	NN
10_1101-2020_10_26_351783	96	7	version	version	NN
10_1101-2020_10_26_351783	96	8	posted	post	VBD
10_1101-2020_10_26_351783	96	9	January	January	NNP
10_1101-2020_10_26_351783	96	10	6	6	CD
10_1101-2020_10_26_351783	96	11	,	,	,
10_1101-2020_10_26_351783	96	12	2021	2021	CD
10_1101-2020_10_26_351783	96	13	.	.	.
10_1101-2020_10_26_351783	96	14	;	;	:
10_1101-2020_10_26_351783	96	15	https://doi.org/10.1101/2020.10.26.351783doi	https://doi.org/10.1101/2020.10.26.351783doi	ADD
10_1101-2020_10_26_351783	96	16	:	:	:
10_1101-2020_10_26_351783	96	17	bioRxiv	biorxiv	IN
10_1101-2020_10_26_351783	96	18	preprint	preprint	NN
10_1101-2020_10_26_351783	96	19	https://doi.org/10.1101/2020.10.26.351783	https://doi.org/10.1101/2020.10.26.351783	NNP
10_1101-2020_10_26_351783	96	20	8	8	CD
10_1101-2020_10_26_351783	96	21	Validation	validation	NN
10_1101-2020_10_26_351783	96	22	of	of	IN
10_1101-2020_10_26_351783	96	23	modules	module	NNS
10_1101-2020_10_26_351783	96	24	The	the	DT
10_1101-2020_10_26_351783	96	25	final	final	JJ
10_1101-2020_10_26_351783	96	26	modules	module	NNS
10_1101-2020_10_26_351783	96	27	produced	produce	VBN
10_1101-2020_10_26_351783	96	28	from	from	IN
10_1101-2020_10_26_351783	96	29	each	each	DT
10_1101-2020_10_26_351783	96	30	single	single	JJ
10_1101-2020_10_26_351783	96	31	algorithm	algorithm	NN
10_1101-2020_10_26_351783	96	32	and	and	CC
10_1101-2020_10_26_351783	96	33	the	the	DT
10_1101-2020_10_26_351783	96	34	consensus	consensus	NN
10_1101-2020_10_26_351783	96	35	were	be	VBD
10_1101-2020_10_26_351783	96	36	evaluated	evaluate	VBN
10_1101-2020_10_26_351783	96	37	using	use	VBG
10_1101-2020_10_26_351783	96	38	Pascal[18	pascal[18	ADD
10_1101-2020_10_26_351783	96	39	]	]	-RRB-
10_1101-2020_10_26_351783	96	40	(	(	-LRB-
10_1101-2020_10_26_351783	96	41	Pathway	Pathway	NNP
10_1101-2020_10_26_351783	96	42	scoring	score	VBG
10_1101-2020_10_26_351783	96	43	algorithm	algorithm	NN
10_1101-2020_10_26_351783	96	44	)	)	-RRB-
10_1101-2020_10_26_351783	96	45	.	.	.
10_1101-2020_10_26_351783	97	1	Pascal	pascal	JJ
10_1101-2020_10_26_351783	97	2	implements	implement	NNS
10_1101-2020_10_26_351783	97	3	a	a	DT
10_1101-2020_10_26_351783	97	4	fast	fast	JJ
10_1101-2020_10_26_351783	97	5	and	and	CC
10_1101-2020_10_26_351783	97	6	rigorous	rigorous	JJ
10_1101-2020_10_26_351783	97	7	gene	gene	NN
10_1101-2020_10_26_351783	97	8	scoring	scoring	NN
10_1101-2020_10_26_351783	97	9	and	and	CC
10_1101-2020_10_26_351783	97	10	pathway	pathway	JJ
10_1101-2020_10_26_351783	97	11	enrichment	enrichment	NN
10_1101-2020_10_26_351783	97	12	pipeline	pipeline	NN
10_1101-2020_10_26_351783	97	13	that	that	WDT
10_1101-2020_10_26_351783	97	14	can	can	MD
10_1101-2020_10_26_351783	97	15	be	be	VB
10_1101-2020_10_26_351783	97	16	run	run	VBN
10_1101-2020_10_26_351783	97	17	on	on	IN
10_1101-2020_10_26_351783	97	18	a	a	DT
10_1101-2020_10_26_351783	97	19	local	local	JJ
10_1101-2020_10_26_351783	97	20	machine	machine	NN
10_1101-2020_10_26_351783	97	21	.	.	.
10_1101-2020_10_26_351783	98	1	The	the	DT
10_1101-2020_10_26_351783	98	2	SNP	SNP	NNP
10_1101-2020_10_26_351783	98	3	values	value	NNS
10_1101-2020_10_26_351783	98	4	are	be	VBP
10_1101-2020_10_26_351783	98	5	converted	convert	VBN
10_1101-2020_10_26_351783	98	6	to	to	IN
10_1101-2020_10_26_351783	98	7	gene	gene	NN
10_1101-2020_10_26_351783	98	8	scores	score	NNS
10_1101-2020_10_26_351783	98	9	by	by	IN
10_1101-2020_10_26_351783	98	10	computing	compute	VBG
10_1101-2020_10_26_351783	98	11	pairwise	pairwise	NN
10_1101-2020_10_26_351783	98	12	SNP	SNP	NNP
10_1101-2020_10_26_351783	98	13	-	-	HYPH
10_1101-2020_10_26_351783	98	14	by	by	IN
10_1101-2020_10_26_351783	98	15	-	-	HYPH
10_1101-2020_10_26_351783	98	16	SNP	SNP	NNP
10_1101-2020_10_26_351783	98	17	correlations	correlation	NNS
10_1101-2020_10_26_351783	98	18	and	and	CC
10_1101-2020_10_26_351783	98	19	obtaining	obtain	VBG
10_1101-2020_10_26_351783	98	20	Z	z	NN
10_1101-2020_10_26_351783	98	21	-	-	HYPH
10_1101-2020_10_26_351783	98	22	scores	score	NNS
10_1101-2020_10_26_351783	98	23	from	from	IN
10_1101-2020_10_26_351783	98	24	their	-PRON-	PRP$
10_1101-2020_10_26_351783	98	25	distribution	distribution	NN
10_1101-2020_10_26_351783	98	26	.	.	.
10_1101-2020_10_26_351783	99	1	These	these	DT
10_1101-2020_10_26_351783	99	2	obtained	obtain	VBN
10_1101-2020_10_26_351783	99	3	gene	gene	NN
10_1101-2020_10_26_351783	99	4	scores	score	NNS
10_1101-2020_10_26_351783	99	5	are	be	VBP
10_1101-2020_10_26_351783	99	6	fused	fuse	VBN
10_1101-2020_10_26_351783	99	7	with	with	IN
10_1101-2020_10_26_351783	99	8	the	the	DT
10_1101-2020_10_26_351783	99	9	pathway	pathway	JJ
10_1101-2020_10_26_351783	99	10	enrichment	enrichment	NN
10_1101-2020_10_26_351783	99	11	analysis	analysis	NN
10_1101-2020_10_26_351783	99	12	to	to	TO
10_1101-2020_10_26_351783	99	13	recompute	recompute	VB
10_1101-2020_10_26_351783	99	14	a	a	DT
10_1101-2020_10_26_351783	99	15	chi	chi	JJ
10_1101-2020_10_26_351783	99	16	-	-	HYPH
10_1101-2020_10_26_351783	99	17	square	square	JJ
10_1101-2020_10_26_351783	99	18	P	p	NN
10_1101-2020_10_26_351783	99	19	-	-	HYPH
10_1101-2020_10_26_351783	99	20	value	value	NN
10_1101-2020_10_26_351783	99	21	for	for	IN
10_1101-2020_10_26_351783	99	22	the	the	DT
10_1101-2020_10_26_351783	99	23	given	give	VBN
10_1101-2020_10_26_351783	99	24	set	set	NN
10_1101-2020_10_26_351783	99	25	of	of	IN
10_1101-2020_10_26_351783	99	26	module	module	JJ
10_1101-2020_10_26_351783	99	27	genes	gene	NNS
10_1101-2020_10_26_351783	99	28	.	.	.
10_1101-2020_10_26_351783	100	1	Thus	thus	RB
10_1101-2020_10_26_351783	100	2	,	,	,
10_1101-2020_10_26_351783	100	3	the	the	DT
10_1101-2020_10_26_351783	100	4	obtained	obtain	VBN
10_1101-2020_10_26_351783	100	5	chi	chi	NNP
10_1101-2020_10_26_351783	100	6	-	-	HYPH
10_1101-2020_10_26_351783	100	7	square	square	JJ
10_1101-2020_10_26_351783	100	8	P-	P-	NNP
10_1101-2020_10_26_351783	100	9	value	value	NN
10_1101-2020_10_26_351783	100	10	serves	serve	VBZ
10_1101-2020_10_26_351783	100	11	as	as	IN
10_1101-2020_10_26_351783	100	12	the	the	DT
10_1101-2020_10_26_351783	100	13	significance	significance	NN
10_1101-2020_10_26_351783	100	14	of	of	IN
10_1101-2020_10_26_351783	100	15	the	the	DT
10_1101-2020_10_26_351783	100	16	module	module	NN
10_1101-2020_10_26_351783	100	17	in	in	IN
10_1101-2020_10_26_351783	100	18	its	-PRON-	PRP$
10_1101-2020_10_26_351783	100	19	enrichment	enrichment	NN
10_1101-2020_10_26_351783	100	20	of	of	IN
10_1101-2020_10_26_351783	100	21	the	the	DT
10_1101-2020_10_26_351783	100	22	disease	disease	NN
10_1101-2020_10_26_351783	100	23	-	-	HYPH
10_1101-2020_10_26_351783	100	24	associated	associate	VBN
10_1101-2020_10_26_351783	100	25	pathway	pathway	JJ
10_1101-2020_10_26_351783	100	26	gene	gene	NN
10_1101-2020_10_26_351783	100	27	loci	loci	NNP
10_1101-2020_10_26_351783	100	28	.	.	.
10_1101-2020_10_26_351783	101	1	A	a	DT
10_1101-2020_10_26_351783	101	2	combined	combined	JJ
10_1101-2020_10_26_351783	101	3	P	p	NN
10_1101-2020_10_26_351783	101	4	-	-	HYPH
10_1101-2020_10_26_351783	101	5	value	value	NN
10_1101-2020_10_26_351783	101	6	was	be	VBD
10_1101-2020_10_26_351783	101	7	computed	compute	VBN
10_1101-2020_10_26_351783	101	8	for	for	IN
10_1101-2020_10_26_351783	101	9	each	each	DT
10_1101-2020_10_26_351783	101	10	of	of	IN
10_1101-2020_10_26_351783	101	11	the	the	DT
10_1101-2020_10_26_351783	101	12	methods	method	NNS
10_1101-2020_10_26_351783	101	13	using	use	VBG
10_1101-2020_10_26_351783	101	14	Fisher	Fisher	NNP
10_1101-2020_10_26_351783	101	15	’s	’s	POS
10_1101-2020_10_26_351783	101	16	method[19	method[19	NN
10_1101-2020_10_26_351783	101	17	]	]	-RRB-
10_1101-2020_10_26_351783	101	18	,	,	,
10_1101-2020_10_26_351783	101	19	diseases	disease	NNS
10_1101-2020_10_26_351783	101	20	,	,	,
10_1101-2020_10_26_351783	101	21	and	and	CC
10_1101-2020_10_26_351783	101	22	datasets	dataset	NNS
10_1101-2020_10_26_351783	101	23	for	for	IN
10_1101-2020_10_26_351783	101	24	ranking	rank	VBG
10_1101-2020_10_26_351783	101	25	the	the	DT
10_1101-2020_10_26_351783	101	26	performance	performance	NN
10_1101-2020_10_26_351783	101	27	of	of	IN
10_1101-2020_10_26_351783	101	28	the	the	DT
10_1101-2020_10_26_351783	101	29	modules	module	NNS
10_1101-2020_10_26_351783	101	30	in	in	IN
10_1101-2020_10_26_351783	101	31	each	each	DT
10_1101-2020_10_26_351783	101	32	criterion	criterion	NN
10_1101-2020_10_26_351783	101	33	.	.	.
10_1101-2020_10_26_351783	102	1	Integration	integration	NN
10_1101-2020_10_26_351783	102	2	of	of	IN
10_1101-2020_10_26_351783	102	3	MS	MS	NNP
10_1101-2020_10_26_351783	102	4	single	single	JJ
10_1101-2020_10_26_351783	102	5	-	-	HYPH
10_1101-2020_10_26_351783	102	6	omic	omic	JJ
10_1101-2020_10_26_351783	102	7	modules	module	NNS
10_1101-2020_10_26_351783	102	8	Clique	Clique	NNP
10_1101-2020_10_26_351783	102	9	SuM	sum	NN
10_1101-2020_10_26_351783	102	10	was	be	VBD
10_1101-2020_10_26_351783	102	11	ranked	rank	VBN
10_1101-2020_10_26_351783	102	12	as	as	IN
10_1101-2020_10_26_351783	102	13	the	the	DT
10_1101-2020_10_26_351783	102	14	best	good	JJS
10_1101-2020_10_26_351783	102	15	performing	performing	NN
10_1101-2020_10_26_351783	102	16	method	method	NN
10_1101-2020_10_26_351783	102	17	on	on	IN
10_1101-2020_10_26_351783	102	18	average	average	NN
10_1101-2020_10_26_351783	102	19	for	for	IN
10_1101-2020_10_26_351783	102	20	both	both	CC
10_1101-2020_10_26_351783	102	21	transcriptomic	transcriptomic	JJ
10_1101-2020_10_26_351783	102	22	and	and	CC
10_1101-2020_10_26_351783	102	23	methylomic	methylomic	JJ
10_1101-2020_10_26_351783	102	24	data	datum	NNS
10_1101-2020_10_26_351783	102	25	,	,	,
10_1101-2020_10_26_351783	102	26	according	accord	VBG
10_1101-2020_10_26_351783	102	27	to	to	IN
10_1101-2020_10_26_351783	102	28	the	the	DT
10_1101-2020_10_26_351783	102	29	MS	MS	NNP
10_1101-2020_10_26_351783	102	30	GWAS	GWAS	NNP
10_1101-2020_10_26_351783	102	31	enrichment	enrichment	NN
10_1101-2020_10_26_351783	102	32	of	of	IN
10_1101-2020_10_26_351783	102	33	the	the	DT
10_1101-2020_10_26_351783	102	34	modules	module	NNS
10_1101-2020_10_26_351783	102	35	calculated	calculate	VBN
10_1101-2020_10_26_351783	102	36	by	by	IN
10_1101-2020_10_26_351783	102	37	Pascal	Pascal	NNP
10_1101-2020_10_26_351783	102	38	.	.	.
10_1101-2020_10_26_351783	103	1	Therefore	therefore	RB
10_1101-2020_10_26_351783	103	2	,	,	,
10_1101-2020_10_26_351783	103	3	significant	significant	JJ
10_1101-2020_10_26_351783	103	4	Clique	Clique	NNP
10_1101-2020_10_26_351783	103	5	SuM	sum	NN
10_1101-2020_10_26_351783	103	6	modules	module	NNS
10_1101-2020_10_26_351783	103	7	(	(	-LRB-
10_1101-2020_10_26_351783	103	8	P	p	NN
10_1101-2020_10_26_351783	103	9	<	<	XX
10_1101-2020_10_26_351783	103	10	0.05	0.05	XX
10_1101-2020_10_26_351783	103	11	)	)	-RRB-
10_1101-2020_10_26_351783	103	12	were	be	VBD
10_1101-2020_10_26_351783	103	13	selected	select	VBN
10_1101-2020_10_26_351783	103	14	for	for	IN
10_1101-2020_10_26_351783	103	15	further	further	JJ
10_1101-2020_10_26_351783	103	16	analysis	analysis	NN
10_1101-2020_10_26_351783	103	17	(	(	-LRB-
10_1101-2020_10_26_351783	103	18	nine	nine	CD
10_1101-2020_10_26_351783	103	19	transcriptomic	transcriptomic	NNP
10_1101-2020_10_26_351783	103	20	and	and	CC
10_1101-2020_10_26_351783	103	21	four	four	CD
10_1101-2020_10_26_351783	103	22	methylomic	methylomic	JJ
10_1101-2020_10_26_351783	103	23	modules	module	NNS
10_1101-2020_10_26_351783	103	24	)	)	-RRB-
10_1101-2020_10_26_351783	103	25	.	.	.
10_1101-2020_10_26_351783	104	1	Consensus	consensus	NN
10_1101-2020_10_26_351783	104	2	modules	module	NNS
10_1101-2020_10_26_351783	104	3	were	be	VBD
10_1101-2020_10_26_351783	104	4	generated	generate	VBN
10_1101-2020_10_26_351783	104	5	across	across	IN
10_1101-2020_10_26_351783	104	6	each	each	DT
10_1101-2020_10_26_351783	104	7	omic	omic	JJ
10_1101-2020_10_26_351783	104	8	by	by	IN
10_1101-2020_10_26_351783	104	9	applying	apply	VBG
10_1101-2020_10_26_351783	104	10	a	a	DT
10_1101-2020_10_26_351783	104	11	module	module	JJ
10_1101-2020_10_26_351783	104	12	count	count	NN
10_1101-2020_10_26_351783	104	13	-	-	HYPH
10_1101-2020_10_26_351783	104	14	based	base	VBN
10_1101-2020_10_26_351783	104	15	method	method	NN
10_1101-2020_10_26_351783	104	16	,	,	,
10_1101-2020_10_26_351783	104	17	where	where	WRB
10_1101-2020_10_26_351783	104	18	the	the	DT
10_1101-2020_10_26_351783	104	19	criteria	criterion	NNS
10_1101-2020_10_26_351783	104	20	for	for	IN
10_1101-2020_10_26_351783	104	21	gene	gene	NN
10_1101-2020_10_26_351783	104	22	inclusion	inclusion	NN
10_1101-2020_10_26_351783	104	23	in	in	IN
10_1101-2020_10_26_351783	104	24	the	the	DT
10_1101-2020_10_26_351783	104	25	consensus	consensus	NN
10_1101-2020_10_26_351783	104	26	is	be	VBZ
10_1101-2020_10_26_351783	104	27	its	-PRON-	PRP$
10_1101-2020_10_26_351783	104	28	presence	presence	NN
10_1101-2020_10_26_351783	104	29	in	in	IN
10_1101-2020_10_26_351783	104	30	a	a	DT
10_1101-2020_10_26_351783	104	31	certain	certain	JJ
10_1101-2020_10_26_351783	104	32	number	number	NN
10_1101-2020_10_26_351783	104	33	of	of	IN
10_1101-2020_10_26_351783	104	34	single	single	JJ
10_1101-2020_10_26_351783	104	35	-	-	HYPH
10_1101-2020_10_26_351783	104	36	method	method	NN
10_1101-2020_10_26_351783	104	37	modules	module	NNS
10_1101-2020_10_26_351783	104	38	.	.	.
10_1101-2020_10_26_351783	105	1	To	to	TO
10_1101-2020_10_26_351783	105	2	balance	balance	VB
10_1101-2020_10_26_351783	105	3	the	the	DT
10_1101-2020_10_26_351783	105	4	weight	weight	NN
10_1101-2020_10_26_351783	105	5	of	of	IN
10_1101-2020_10_26_351783	105	6	each	each	DT
10_1101-2020_10_26_351783	105	7	omic	omic	JJ
10_1101-2020_10_26_351783	105	8	in	in	IN
10_1101-2020_10_26_351783	105	9	the	the	DT
10_1101-2020_10_26_351783	105	10	multi	multi	JJ
10_1101-2020_10_26_351783	105	11	-	-	NNS
10_1101-2020_10_26_351783	105	12	omics	omic	NNS
10_1101-2020_10_26_351783	105	13	integration	integration	NN
10_1101-2020_10_26_351783	105	14	,	,	,
10_1101-2020_10_26_351783	105	15	the	the	DT
10_1101-2020_10_26_351783	105	16	top	top	JJ
10_1101-2020_10_26_351783	105	17	four	four	CD
10_1101-2020_10_26_351783	105	18	significant	significant	JJ
10_1101-2020_10_26_351783	105	19	modules	module	NNS
10_1101-2020_10_26_351783	105	20	per	per	IN
10_1101-2020_10_26_351783	105	21	omic	omic	JJ
10_1101-2020_10_26_351783	105	22	were	be	VBD
10_1101-2020_10_26_351783	105	23	used	use	VBN
10_1101-2020_10_26_351783	105	24	to	to	TO
10_1101-2020_10_26_351783	105	25	create	create	VB
10_1101-2020_10_26_351783	105	26	each	each	DT
10_1101-2020_10_26_351783	105	27	consensus	consensus	NN
10_1101-2020_10_26_351783	105	28	(	(	-LRB-
10_1101-2020_10_26_351783	105	29	Fig	fig	NN
10_1101-2020_10_26_351783	105	30	.	.	.
10_1101-2020_10_26_351783	106	1	4a	4a	NNP
10_1101-2020_10_26_351783	106	2	,	,	,
10_1101-2020_10_26_351783	106	3	b	b	NN
10_1101-2020_10_26_351783	106	4	)	)	-RRB-
10_1101-2020_10_26_351783	106	5	.	.	.
10_1101-2020_10_26_351783	107	1	Single	single	JJ
10_1101-2020_10_26_351783	107	2	-	-	HYPH
10_1101-2020_10_26_351783	107	3	omic	omic	JJ
10_1101-2020_10_26_351783	107	4	Clique	Clique	NNP
10_1101-2020_10_26_351783	107	5	SuM	sum	NN
10_1101-2020_10_26_351783	107	6	consensus	consensus	NN
10_1101-2020_10_26_351783	107	7	were	be	VBD
10_1101-2020_10_26_351783	107	8	ranked	rank	VBN
10_1101-2020_10_26_351783	107	9	again	again	RB
10_1101-2020_10_26_351783	107	10	by	by	IN
10_1101-2020_10_26_351783	107	11	GWAS	GWAS	NNP
10_1101-2020_10_26_351783	107	12	enrichment	enrichment	NN
10_1101-2020_10_26_351783	107	13	,	,	,
10_1101-2020_10_26_351783	107	14	and	and	CC
10_1101-2020_10_26_351783	107	15	the	the	DT
10_1101-2020_10_26_351783	107	16	best	good	JJS
10_1101-2020_10_26_351783	107	17	performing	perform	VBG
10_1101-2020_10_26_351783	107	18	consensus	consensus	NN
10_1101-2020_10_26_351783	107	19	per	per	IN
10_1101-2020_10_26_351783	107	20	omic	omic	JJ
10_1101-2020_10_26_351783	107	21	was	be	VBD
10_1101-2020_10_26_351783	107	22	selected	select	VBN
10_1101-2020_10_26_351783	107	23	for	for	IN
10_1101-2020_10_26_351783	107	24	integration	integration	NN
10_1101-2020_10_26_351783	107	25	into	into	IN
10_1101-2020_10_26_351783	107	26	the	the	DT
10_1101-2020_10_26_351783	107	27	multi	multi	JJ
10_1101-2020_10_26_351783	107	28	-	-	JJ
10_1101-2020_10_26_351783	107	29	omics	omics	JJ
10_1101-2020_10_26_351783	107	30	module	module	NN
10_1101-2020_10_26_351783	107	31	.	.	.
10_1101-2020_10_26_351783	108	1	Enrichment	enrichment	NN
10_1101-2020_10_26_351783	108	2	analyses	analysis	NNS
10_1101-2020_10_26_351783	108	3	of	of	IN
10_1101-2020_10_26_351783	108	4	the	the	DT
10_1101-2020_10_26_351783	108	5	MS	MS	NNP
10_1101-2020_10_26_351783	108	6	multi	multi	JJ
10_1101-2020_10_26_351783	108	7	-	-	HYPH
10_1101-2020_10_26_351783	108	8	omics	omics	JJ
10_1101-2020_10_26_351783	108	9	module	module	NNP
10_1101-2020_10_26_351783	108	10	Disease	Disease	NNP
10_1101-2020_10_26_351783	108	11	enrichment	enrichment	NN
10_1101-2020_10_26_351783	108	12	analysis	analysis	NN
10_1101-2020_10_26_351783	108	13	of	of	IN
10_1101-2020_10_26_351783	108	14	the	the	DT
10_1101-2020_10_26_351783	108	15	multi	multi	JJ
10_1101-2020_10_26_351783	108	16	-	-	NNS
10_1101-2020_10_26_351783	108	17	omics	omic	NNS
10_1101-2020_10_26_351783	108	18	module	module	NN
10_1101-2020_10_26_351783	108	19	was	be	VBD
10_1101-2020_10_26_351783	108	20	performed	perform	VBN
10_1101-2020_10_26_351783	108	21	by	by	IN
10_1101-2020_10_26_351783	108	22	Fisher	Fisher	NNP
10_1101-2020_10_26_351783	108	23	’s	’s	POS
10_1101-2020_10_26_351783	108	24	exact	exact	JJ
10_1101-2020_10_26_351783	108	25	test	test	NN
10_1101-2020_10_26_351783	108	26	,	,	,
10_1101-2020_10_26_351783	108	27	with	with	IN
10_1101-2020_10_26_351783	108	28	a	a	DT
10_1101-2020_10_26_351783	108	29	significance	significance	NN
10_1101-2020_10_26_351783	108	30	threshold	threshold	NN
10_1101-2020_10_26_351783	108	31	of	of	IN
10_1101-2020_10_26_351783	108	32	P	p	NN
10_1101-2020_10_26_351783	108	33	<	<	XX
10_1101-2020_10_26_351783	108	34	0.05	0.05	CD
10_1101-2020_10_26_351783	108	35	.	.	.
10_1101-2020_10_26_351783	109	1	MS	MS	NNP
10_1101-2020_10_26_351783	109	2	-	-	HYPH
10_1101-2020_10_26_351783	109	3	associated	associate	VBN
10_1101-2020_10_26_351783	109	4	genes	gene	NNS
10_1101-2020_10_26_351783	109	5	were	be	VBD
10_1101-2020_10_26_351783	109	6	obtained	obtain	VBN
10_1101-2020_10_26_351783	109	7	from	from	IN
10_1101-2020_10_26_351783	109	8	the	the	DT
10_1101-2020_10_26_351783	109	9	gene	gene	NN
10_1101-2020_10_26_351783	109	10	-	-	HYPH
10_1101-2020_10_26_351783	109	11	disease	disease	NNP
10_1101-2020_10_26_351783	109	12	association	association	NNP
10_1101-2020_10_26_351783	109	13	summary	summary	NN
10_1101-2020_10_26_351783	109	14	provided	provide	VBN
10_1101-2020_10_26_351783	109	15	by	by	IN
10_1101-2020_10_26_351783	109	16	DisGeNET	DisGeNET	NNP
10_1101-2020_10_26_351783	109	17	database	database	NN
10_1101-2020_10_26_351783	109	18	6.0[20]	6.0[20]	NNP
10_1101-2020_10_26_351783	109	19	�	�	NNP
10_1101-2020_10_26_351783	109	20	.	.	.
10_1101-2020_10_26_351783	110	1	All	all	DT
10_1101-2020_10_26_351783	110	2	genes	gene	NNS
10_1101-2020_10_26_351783	110	3	with	with	IN
10_1101-2020_10_26_351783	110	4	a	a	DT
10_1101-2020_10_26_351783	110	5	known	know	VBN
10_1101-2020_10_26_351783	110	6	association	association	NN
10_1101-2020_10_26_351783	110	7	(	(	-LRB-
10_1101-2020_10_26_351783	110	8	which	which	WDT
10_1101-2020_10_26_351783	110	9	was	be	VBD
10_1101-2020_10_26_351783	110	10	not	not	RB
10_1101-2020_10_26_351783	110	11	certified	certify	VBN
10_1101-2020_10_26_351783	110	12	by	by	IN
10_1101-2020_10_26_351783	110	13	peer	peer	NN
10_1101-2020_10_26_351783	110	14	review	review	NN
10_1101-2020_10_26_351783	110	15	)	)	-RRB-
10_1101-2020_10_26_351783	110	16	is	be	VBZ
10_1101-2020_10_26_351783	110	17	the	the	DT
10_1101-2020_10_26_351783	110	18	author	author	NN
10_1101-2020_10_26_351783	110	19	/	/	SYM
10_1101-2020_10_26_351783	110	20	funder	funder	NN
10_1101-2020_10_26_351783	110	21	.	.	.
10_1101-2020_10_26_351783	111	1	All	all	DT
10_1101-2020_10_26_351783	111	2	rights	right	NNS
10_1101-2020_10_26_351783	111	3	reserved	reserve	VBD
10_1101-2020_10_26_351783	111	4	.	.	.
10_1101-2020_10_26_351783	112	1	No	no	DT
10_1101-2020_10_26_351783	112	2	reuse	reuse	NN
10_1101-2020_10_26_351783	112	3	allowed	allow	VBN
10_1101-2020_10_26_351783	112	4	without	without	IN
10_1101-2020_10_26_351783	112	5	permission	permission	NN
10_1101-2020_10_26_351783	112	6	.	.	.
10_1101-2020_10_26_351783	113	1	The	the	DT
10_1101-2020_10_26_351783	113	2	copyright	copyright	NN
10_1101-2020_10_26_351783	113	3	holder	holder	NN
10_1101-2020_10_26_351783	113	4	for	for	IN
10_1101-2020_10_26_351783	113	5	this	this	DT
10_1101-2020_10_26_351783	113	6	preprintthis	preprintthis	NN
10_1101-2020_10_26_351783	113	7	version	version	NN
10_1101-2020_10_26_351783	113	8	posted	post	VBD
10_1101-2020_10_26_351783	113	9	January	January	NNP
10_1101-2020_10_26_351783	113	10	6	6	CD
10_1101-2020_10_26_351783	113	11	,	,	,
10_1101-2020_10_26_351783	113	12	2021	2021	CD
10_1101-2020_10_26_351783	113	13	.	.	.
10_1101-2020_10_26_351783	113	14	;	;	:
10_1101-2020_10_26_351783	113	15	https://doi.org/10.1101/2020.10.26.351783doi	https://doi.org/10.1101/2020.10.26.351783doi	ADD
10_1101-2020_10_26_351783	113	16	:	:	:
10_1101-2020_10_26_351783	113	17	bioRxiv	biorxiv	VB
10_1101-2020_10_26_351783	113	18	preprint	preprint	NN
10_1101-2020_10_26_351783	113	19	https://doi.org/10.1101/2020.10.26.351783	https://doi.org/10.1101/2020.10.26.351783	CC
10_1101-2020_10_26_351783	113	20	9	9	CD
10_1101-2020_10_26_351783	113	21	to	to	IN
10_1101-2020_10_26_351783	113	22	the	the	DT
10_1101-2020_10_26_351783	113	23	disease	disease	NN
10_1101-2020_10_26_351783	113	24	“	"	``
10_1101-2020_10_26_351783	113	25	multiple	multiple	JJ
10_1101-2020_10_26_351783	113	26	sclerosis	sclerosis	NN
10_1101-2020_10_26_351783	113	27	”	"	''
10_1101-2020_10_26_351783	113	28	(	(	-LRB-
10_1101-2020_10_26_351783	113	29	Unified	Unified	NNP
10_1101-2020_10_26_351783	113	30	Medical	Medical	NNP
10_1101-2020_10_26_351783	113	31	Language	Language	NNP
10_1101-2020_10_26_351783	113	32	System	System	NNP
10_1101-2020_10_26_351783	113	33	unique	unique	JJ
10_1101-2020_10_26_351783	113	34	identifier	identifi	JJR
10_1101-2020_10_26_351783	113	35	C0026769	C0026769	NNP
10_1101-2020_10_26_351783	113	36	)	)	-RRB-
10_1101-2020_10_26_351783	113	37	were	be	VBD
10_1101-2020_10_26_351783	113	38	considered	consider	VBN
10_1101-2020_10_26_351783	113	39	MS	MS	NNP
10_1101-2020_10_26_351783	113	40	-	-	HYPH
10_1101-2020_10_26_351783	113	41	associated	associate	VBN
10_1101-2020_10_26_351783	113	42	genes	gene	NNS
10_1101-2020_10_26_351783	113	43	(	(	-LRB-
10_1101-2020_10_26_351783	113	44	n	n	NNP
10_1101-2020_10_26_351783	113	45	=	=	SYM
10_1101-2020_10_26_351783	113	46	1,105	1,105	CD
10_1101-2020_10_26_351783	113	47	)	)	-RRB-
10_1101-2020_10_26_351783	113	48	.	.	.
10_1101-2020_10_26_351783	114	1	Pathway	pathway	JJ
10_1101-2020_10_26_351783	114	2	enrichment	enrichment	NN
10_1101-2020_10_26_351783	114	3	analysis	analysis	NN
10_1101-2020_10_26_351783	114	4	was	be	VBD
10_1101-2020_10_26_351783	114	5	carried	carry	VBN
10_1101-2020_10_26_351783	114	6	out	out	RP
10_1101-2020_10_26_351783	114	7	using	use	VBG
10_1101-2020_10_26_351783	114	8	the	the	DT
10_1101-2020_10_26_351783	114	9	function	function	NN
10_1101-2020_10_26_351783	114	10	enrichKEGG	enrichKEGG	NNP
10_1101-2020_10_26_351783	114	11	from	from	IN
10_1101-2020_10_26_351783	114	12	the	the	DT
10_1101-2020_10_26_351783	114	13	clusterProfiler	clusterprofiler	NN
10_1101-2020_10_26_351783	114	14	R	R	NNP
10_1101-2020_10_26_351783	114	15	package[21	package[21	NNP
10_1101-2020_10_26_351783	114	16	]	]	-RRB-
10_1101-2020_10_26_351783	114	17	�	�	NNP
10_1101-2020_10_26_351783	114	18	,	,	,
10_1101-2020_10_26_351783	114	19	version	version	NN
10_1101-2020_10_26_351783	114	20	3.14.3	3.14.3	CD
10_1101-2020_10_26_351783	114	21	.	.	.
10_1101-2020_10_26_351783	115	1	P	p	NN
10_1101-2020_10_26_351783	115	2	-	-	HYPH
10_1101-2020_10_26_351783	115	3	values	value	NNS
10_1101-2020_10_26_351783	115	4	were	be	VBD
10_1101-2020_10_26_351783	115	5	adjusted	adjust	VBN
10_1101-2020_10_26_351783	115	6	for	for	IN
10_1101-2020_10_26_351783	115	7	multiple	multiple	JJ
10_1101-2020_10_26_351783	115	8	testing	testing	NN
10_1101-2020_10_26_351783	115	9	using	use	VBG
10_1101-2020_10_26_351783	115	10	Benjamini	Benjamini	NNP
10_1101-2020_10_26_351783	115	11	-	-	HYPH
10_1101-2020_10_26_351783	115	12	Hochberg	Hochberg	NNP
10_1101-2020_10_26_351783	115	13	FDR	FDR	NNP
10_1101-2020_10_26_351783	115	14	correction	correction	NN
10_1101-2020_10_26_351783	115	15	,	,	,
10_1101-2020_10_26_351783	115	16	with	with	IN
10_1101-2020_10_26_351783	115	17	a	a	DT
10_1101-2020_10_26_351783	115	18	significance	significance	NN
10_1101-2020_10_26_351783	115	19	threshold	threshold	NN
10_1101-2020_10_26_351783	115	20	of	of	IN
10_1101-2020_10_26_351783	115	21	adj	adj	NN
10_1101-2020_10_26_351783	115	22	.	.	.
10_1101-2020_10_26_351783	116	1	P	p	NN
10_1101-2020_10_26_351783	116	2	<	<	XX
10_1101-2020_10_26_351783	116	3	0.05	0.05	XX
10_1101-2020_10_26_351783	116	4	.	.	.
10_1101-2020_10_26_351783	117	1	Enrichment	enrichment	NN
10_1101-2020_10_26_351783	117	2	of	of	IN
10_1101-2020_10_26_351783	117	3	the	the	DT
10_1101-2020_10_26_351783	117	4	multi	multi	JJ
10_1101-2020_10_26_351783	117	5	-	-	JJ
10_1101-2020_10_26_351783	117	6	omics	omics	JJ
10_1101-2020_10_26_351783	117	7	module	module	NN
10_1101-2020_10_26_351783	117	8	in	in	IN
10_1101-2020_10_26_351783	117	9	MS	MS	NNP
10_1101-2020_10_26_351783	117	10	risk	risk	NN
10_1101-2020_10_26_351783	117	11	-	-	HYPH
10_1101-2020_10_26_351783	117	12	factor	factor	NN
10_1101-2020_10_26_351783	117	13	-	-	HYPH
10_1101-2020_10_26_351783	117	14	associated	associate	VBN
10_1101-2020_10_26_351783	117	15	genes	gene	NNS
10_1101-2020_10_26_351783	117	16	was	be	VBD
10_1101-2020_10_26_351783	117	17	performed	perform	VBN
10_1101-2020_10_26_351783	117	18	by	by	IN
10_1101-2020_10_26_351783	117	19	Fisher	Fisher	NNP
10_1101-2020_10_26_351783	117	20	’s	’s	POS
10_1101-2020_10_26_351783	117	21	exact	exact	JJ
10_1101-2020_10_26_351783	117	22	test	test	NN
10_1101-2020_10_26_351783	117	23	,	,	,
10_1101-2020_10_26_351783	117	24	with	with	IN
10_1101-2020_10_26_351783	117	25	a	a	DT
10_1101-2020_10_26_351783	117	26	significance	significance	NN
10_1101-2020_10_26_351783	117	27	threshold	threshold	NN
10_1101-2020_10_26_351783	117	28	of	of	IN
10_1101-2020_10_26_351783	117	29	P	p	NN
10_1101-2020_10_26_351783	117	30	<	<	XX
10_1101-2020_10_26_351783	117	31	0.05	0.05	CD
10_1101-2020_10_26_351783	117	32	.	.	.
10_1101-2020_10_26_351783	118	1	To	to	TO
10_1101-2020_10_26_351783	118	2	provide	provide	VB
10_1101-2020_10_26_351783	118	3	a	a	DT
10_1101-2020_10_26_351783	118	4	uniform	uniform	JJ
10_1101-2020_10_26_351783	118	5	comparison	comparison	NN
10_1101-2020_10_26_351783	118	6	of	of	IN
10_1101-2020_10_26_351783	118	7	MS	MS	NNP
10_1101-2020_10_26_351783	118	8	risk	risk	NN
10_1101-2020_10_26_351783	118	9	factor	factor	NN
10_1101-2020_10_26_351783	118	10	-	-	HYPH
10_1101-2020_10_26_351783	118	11	associated	associate	VBN
10_1101-2020_10_26_351783	118	12	genes	gene	NNS
10_1101-2020_10_26_351783	118	13	across	across	IN
10_1101-2020_10_26_351783	118	14	datasets	dataset	NNS
10_1101-2020_10_26_351783	118	15	,	,	,
10_1101-2020_10_26_351783	118	16	the	the	DT
10_1101-2020_10_26_351783	118	17	module	module	NN
10_1101-2020_10_26_351783	118	18	was	be	VBD
10_1101-2020_10_26_351783	118	19	tested	test	VBN
10_1101-2020_10_26_351783	118	20	for	for	IN
10_1101-2020_10_26_351783	118	21	enrichment	enrichment	NN
10_1101-2020_10_26_351783	118	22	in	in	IN
10_1101-2020_10_26_351783	118	23	the	the	DT
10_1101-2020_10_26_351783	118	24	top	top	JJ
10_1101-2020_10_26_351783	118	25	1,000	1,000	CD
10_1101-2020_10_26_351783	118	26	DMGs	dmg	NNS
10_1101-2020_10_26_351783	118	27	(	(	-LRB-
10_1101-2020_10_26_351783	118	28	with	with	IN
10_1101-2020_10_26_351783	118	29	at	at	RB
10_1101-2020_10_26_351783	118	30	least	least	JJS
10_1101-2020_10_26_351783	118	31	P	p	NN
10_1101-2020_10_26_351783	118	32	<	<	XX
10_1101-2020_10_26_351783	118	33	0.05	0.05	XX
10_1101-2020_10_26_351783	118	34	)	)	-RRB-
10_1101-2020_10_26_351783	118	35	obtained	obtain	VBN
10_1101-2020_10_26_351783	118	36	from	from	IN
10_1101-2020_10_26_351783	118	37	the	the	DT
10_1101-2020_10_26_351783	118	38	differential	differential	NN
10_1101-2020_10_26_351783	118	39	methylation	methylation	NN
10_1101-2020_10_26_351783	118	40	analysis	analysis	NN
10_1101-2020_10_26_351783	118	41	with	with	IN
10_1101-2020_10_26_351783	118	42	ChAMP	ChAMP	NNP
10_1101-2020_10_26_351783	118	43	for	for	IN
10_1101-2020_10_26_351783	118	44	each	each	DT
10_1101-2020_10_26_351783	118	45	risk	risk	NN
10_1101-2020_10_26_351783	118	46	factor	factor	NN
10_1101-2020_10_26_351783	118	47	dataset	dataset	NN
10_1101-2020_10_26_351783	118	48	.	.	.
10_1101-2020_10_26_351783	119	1	Representation	representation	NN
10_1101-2020_10_26_351783	119	2	of	of	IN
10_1101-2020_10_26_351783	119	3	the	the	DT
10_1101-2020_10_26_351783	119	4	MS	MS	NNP
10_1101-2020_10_26_351783	119	5	multi	multi	JJ
10_1101-2020_10_26_351783	119	6	-	-	HYPH
10_1101-2020_10_26_351783	119	7	omics	omic	NNS
10_1101-2020_10_26_351783	119	8	module	module	NN
10_1101-2020_10_26_351783	119	9	Experimentally	experimentally	RB
10_1101-2020_10_26_351783	119	10	validated	validate	VBD
10_1101-2020_10_26_351783	119	11	interactions	interaction	NNS
10_1101-2020_10_26_351783	119	12	for	for	IN
10_1101-2020_10_26_351783	119	13	the	the	DT
10_1101-2020_10_26_351783	119	14	multi	multi	JJ
10_1101-2020_10_26_351783	119	15	-	-	JJ
10_1101-2020_10_26_351783	119	16	omics	omics	JJ
10_1101-2020_10_26_351783	119	17	module	module	JJ
10_1101-2020_10_26_351783	119	18	genes	gene	NNS
10_1101-2020_10_26_351783	119	19	were	be	VBD
10_1101-2020_10_26_351783	119	20	obtained	obtain	VBN
10_1101-2020_10_26_351783	119	21	from	from	IN
10_1101-2020_10_26_351783	119	22	STRING	string	NN
10_1101-2020_10_26_351783	119	23	database	database	NN
10_1101-2020_10_26_351783	119	24	version	version	NN
10_1101-2020_10_26_351783	119	25	11	11	CD
10_1101-2020_10_26_351783	119	26	(	(	-LRB-
10_1101-2020_10_26_351783	119	27	experimental	experimental	JJ
10_1101-2020_10_26_351783	119	28	score	score	NN
10_1101-2020_10_26_351783	119	29	>	>	XX
10_1101-2020_10_26_351783	119	30	700	700	CD
10_1101-2020_10_26_351783	119	31	)	)	-RRB-
10_1101-2020_10_26_351783	119	32	and	and	CC
10_1101-2020_10_26_351783	119	33	imported	import	VBN
10_1101-2020_10_26_351783	119	34	into	into	IN
10_1101-2020_10_26_351783	119	35	Cytoscape[22	Cytoscape[22	NNP
10_1101-2020_10_26_351783	119	36	]	]	-RRB-
10_1101-2020_10_26_351783	119	37	version	version	NN
10_1101-2020_10_26_351783	119	38	3.7.2	3.7.2	CD
10_1101-2020_10_26_351783	119	39	.	.	.
10_1101-2020_10_26_351783	120	1	To	to	TO
10_1101-2020_10_26_351783	120	2	determine	determine	VB
10_1101-2020_10_26_351783	120	3	representative	representative	JJ
10_1101-2020_10_26_351783	120	4	functional	functional	JJ
10_1101-2020_10_26_351783	120	5	clusters	cluster	NNS
10_1101-2020_10_26_351783	120	6	of	of	IN
10_1101-2020_10_26_351783	120	7	module	module	JJ
10_1101-2020_10_26_351783	120	8	genes	gene	NNS
10_1101-2020_10_26_351783	120	9	,	,	,
10_1101-2020_10_26_351783	120	10	overrepresented	overrepresente	VBD
10_1101-2020_10_26_351783	120	11	Gene	Gene	NNP
10_1101-2020_10_26_351783	120	12	Ontology	Ontology	NNP
10_1101-2020_10_26_351783	120	13	(	(	-LRB-
10_1101-2020_10_26_351783	120	14	GO	GO	NNP
10_1101-2020_10_26_351783	120	15	)	)	-RRB-
10_1101-2020_10_26_351783	120	16	Biological	Biological	NNP
10_1101-2020_10_26_351783	120	17	Process	Process	NNP
10_1101-2020_10_26_351783	120	18	(	(	-LRB-
10_1101-2020_10_26_351783	120	19	BP	BP	NNP
10_1101-2020_10_26_351783	120	20	)	)	-RRB-
10_1101-2020_10_26_351783	120	21	terms	term	NNS
10_1101-2020_10_26_351783	120	22	in	in	IN
10_1101-2020_10_26_351783	120	23	the	the	DT
10_1101-2020_10_26_351783	120	24	module	module	NN
10_1101-2020_10_26_351783	120	25	were	be	VBD
10_1101-2020_10_26_351783	120	26	found	find	VBN
10_1101-2020_10_26_351783	120	27	using	use	VBG
10_1101-2020_10_26_351783	120	28	BiNGO[23	BiNGO[23	NNP
10_1101-2020_10_26_351783	120	29	]	]	-RRB-
10_1101-2020_10_26_351783	120	30	version	version	NN
10_1101-2020_10_26_351783	120	31	3.0.4	3.0.4	CD
10_1101-2020_10_26_351783	120	32	,	,	,
10_1101-2020_10_26_351783	120	33	with	with	IN
10_1101-2020_10_26_351783	120	34	Benjamini	Benjamini	NNP
10_1101-2020_10_26_351783	120	35	-	-	HYPH
10_1101-2020_10_26_351783	120	36	Hochberg	Hochberg	NNP
10_1101-2020_10_26_351783	120	37	FDR	FDR	NNP
10_1101-2020_10_26_351783	120	38	for	for	IN
10_1101-2020_10_26_351783	120	39	multiple	multiple	JJ
10_1101-2020_10_26_351783	120	40	testing	testing	NN
10_1101-2020_10_26_351783	120	41	correction	correction	NN
10_1101-2020_10_26_351783	120	42	,	,	,
10_1101-2020_10_26_351783	120	43	and	and	CC
10_1101-2020_10_26_351783	120	44	a	a	DT
10_1101-2020_10_26_351783	120	45	significance	significance	NN
10_1101-2020_10_26_351783	120	46	threshold	threshold	NN
10_1101-2020_10_26_351783	120	47	of	of	IN
10_1101-2020_10_26_351783	120	48	adj	adj	NN
10_1101-2020_10_26_351783	120	49	.	.	.
10_1101-2020_10_26_351783	121	1	P	p	NN
10_1101-2020_10_26_351783	121	2	<	<	XX
10_1101-2020_10_26_351783	121	3	0.05	0.05	XX
10_1101-2020_10_26_351783	121	4	.	.	.
10_1101-2020_10_26_351783	122	1	Then	then	RB
10_1101-2020_10_26_351783	122	2	,	,	,
10_1101-2020_10_26_351783	122	3	enriched	enrich	VBN
10_1101-2020_10_26_351783	122	4	GO	GO	NNP
10_1101-2020_10_26_351783	122	5	terms	term	NNS
10_1101-2020_10_26_351783	122	6	with	with	IN
10_1101-2020_10_26_351783	122	7	adj	adj	NN
10_1101-2020_10_26_351783	122	8	.	.	.
10_1101-2020_10_26_351783	123	1	P	p	NN
10_1101-2020_10_26_351783	123	2	<	<	XX
10_1101-2020_10_26_351783	123	3	1x10	1x10	CD
10_1101-2020_10_26_351783	123	4	-	-	SYM
10_1101-2020_10_26_351783	123	5	10	10	CD
10_1101-2020_10_26_351783	123	6	were	be	VBD
10_1101-2020_10_26_351783	123	7	summarized	summarize	VBN
10_1101-2020_10_26_351783	123	8	using	use	VBG
10_1101-2020_10_26_351783	123	9	REVIGO[24	revigo[24	ADD
10_1101-2020_10_26_351783	123	10	]	]	-RRB-
10_1101-2020_10_26_351783	123	11	server	server	NN
10_1101-2020_10_26_351783	123	12	tool	tool	NN
10_1101-2020_10_26_351783	123	13	(	(	-LRB-
10_1101-2020_10_26_351783	123	14	medium	medium	NNP
10_1101-2020_10_26_351783	123	15	allowed	allow	VBN
10_1101-2020_10_26_351783	123	16	similarity	similarity	NN
10_1101-2020_10_26_351783	123	17	=	=	SYM
10_1101-2020_10_26_351783	123	18	0.7	0.7	CD
10_1101-2020_10_26_351783	123	19	)	)	-RRB-
10_1101-2020_10_26_351783	123	20	and	and	CC
10_1101-2020_10_26_351783	123	21	categories	category	NNS
10_1101-2020_10_26_351783	123	22	of	of	IN
10_1101-2020_10_26_351783	123	23	interest	interest	NN
10_1101-2020_10_26_351783	123	24	were	be	VBD
10_1101-2020_10_26_351783	123	25	selected	select	VBN
10_1101-2020_10_26_351783	123	26	by	by	IN
10_1101-2020_10_26_351783	123	27	uniqueness	uniqueness	NN
10_1101-2020_10_26_351783	123	28	(	(	-LRB-
10_1101-2020_10_26_351783	123	29	>	>	XX
10_1101-2020_10_26_351783	123	30	=	=	SYM
10_1101-2020_10_26_351783	123	31	80	80	CD
10_1101-2020_10_26_351783	123	32	%	%	NN
10_1101-2020_10_26_351783	123	33	)	)	-RRB-
10_1101-2020_10_26_351783	123	34	,	,	,
10_1101-2020_10_26_351783	123	35	dispensability	dispensability	NN
10_1101-2020_10_26_351783	123	36	(	(	-LRB-
10_1101-2020_10_26_351783	123	37	>	>	XX
10_1101-2020_10_26_351783	123	38	=	=	SYM
10_1101-2020_10_26_351783	123	39	50	50	CD
10_1101-2020_10_26_351783	123	40	%	%	NN
10_1101-2020_10_26_351783	123	41	)	)	-RRB-
10_1101-2020_10_26_351783	123	42	,	,	,
10_1101-2020_10_26_351783	123	43	and	and	CC
10_1101-2020_10_26_351783	123	44	frequency	frequency	NN
10_1101-2020_10_26_351783	123	45	(	(	-LRB-
10_1101-2020_10_26_351783	123	46	<	<	XX
10_1101-2020_10_26_351783	123	47	=	=	SYM
10_1101-2020_10_26_351783	123	48	10	10	CD
10_1101-2020_10_26_351783	123	49	%	%	NN
10_1101-2020_10_26_351783	123	50	)	)	-RRB-
10_1101-2020_10_26_351783	123	51	criteria	criterion	NNS
10_1101-2020_10_26_351783	123	52	.	.	.
10_1101-2020_10_26_351783	124	1	Further	further	JJ
10_1101-2020_10_26_351783	124	2	manual	manual	JJ
10_1101-2020_10_26_351783	124	3	assessment	assessment	NN
10_1101-2020_10_26_351783	124	4	was	be	VBD
10_1101-2020_10_26_351783	124	5	performed	perform	VBN
10_1101-2020_10_26_351783	124	6	to	to	TO
10_1101-2020_10_26_351783	124	7	group	group	VB
10_1101-2020_10_26_351783	124	8	similar	similar	JJ
10_1101-2020_10_26_351783	124	9	terms	term	NNS
10_1101-2020_10_26_351783	124	10	with	with	IN
10_1101-2020_10_26_351783	124	11	an	an	DT
10_1101-2020_10_26_351783	124	12	adequate	adequate	JJ
10_1101-2020_10_26_351783	124	13	number	number	NN
10_1101-2020_10_26_351783	124	14	of	of	IN
10_1101-2020_10_26_351783	124	15	genes	gene	NNS
10_1101-2020_10_26_351783	124	16	in	in	IN
10_1101-2020_10_26_351783	124	17	the	the	DT
10_1101-2020_10_26_351783	124	18	network	network	NN
10_1101-2020_10_26_351783	124	19	.	.	.
10_1101-2020_10_26_351783	125	1	RESULTS	RESULTS	NNP
10_1101-2020_10_26_351783	125	2	A	a	DT
10_1101-2020_10_26_351783	125	3	benchmark	benchmark	NN
10_1101-2020_10_26_351783	125	4	comparing	compare	VBG
10_1101-2020_10_26_351783	125	5	337	337	CD
10_1101-2020_10_26_351783	125	6	transcriptionally	transcriptionally	RB
10_1101-2020_10_26_351783	125	7	derived	derive	VBN
10_1101-2020_10_26_351783	125	8	disease	disease	NN
10_1101-2020_10_26_351783	125	9	modules	module	NNS
10_1101-2020_10_26_351783	125	10	from	from	IN
10_1101-2020_10_26_351783	125	11	19	19	CD
10_1101-2020_10_26_351783	125	12	different	different	JJ
10_1101-2020_10_26_351783	125	13	diseases	disease	NNS
10_1101-2020_10_26_351783	125	14	.	.	.
10_1101-2020_10_26_351783	126	1	We	-PRON-	PRP
10_1101-2020_10_26_351783	126	2	compiled	compile	VBD
10_1101-2020_10_26_351783	126	3	a	a	DT
10_1101-2020_10_26_351783	126	4	benchmark	benchmark	JJ
10_1101-2020_10_26_351783	126	5	source	source	NN
10_1101-2020_10_26_351783	126	6	of	of	IN
10_1101-2020_10_26_351783	126	7	disease	disease	NNP
10_1101-2020_10_26_351783	126	8	modules	module	NNS
10_1101-2020_10_26_351783	126	9	and	and	CC
10_1101-2020_10_26_351783	126	10	summary	summary	NN
10_1101-2020_10_26_351783	126	11	statistics	statistic	NNS
10_1101-2020_10_26_351783	126	12	of	of	IN
10_1101-2020_10_26_351783	126	13	GWAS	GWAS	NNP
10_1101-2020_10_26_351783	126	14	datasets	dataset	NNS
10_1101-2020_10_26_351783	126	15	from	from	IN
10_1101-2020_10_26_351783	126	16	19	19	CD
10_1101-2020_10_26_351783	126	17	well	well	RB
10_1101-2020_10_26_351783	126	18	-	-	HYPH
10_1101-2020_10_26_351783	126	19	powered	power	VBN
10_1101-2020_10_26_351783	126	20	case	case	NN
10_1101-2020_10_26_351783	126	21	-	-	HYPH
10_1101-2020_10_26_351783	126	22	control	control	NN
10_1101-2020_10_26_351783	126	23	studies	study	NNS
10_1101-2020_10_26_351783	126	24	(	(	-LRB-
10_1101-2020_10_26_351783	126	25	Supplementary	Supplementary	NNP
10_1101-2020_10_26_351783	126	26	Table	Table	NNP
10_1101-2020_10_26_351783	126	27	1	1	CD
10_1101-2020_10_26_351783	126	28	)	)	-RRB-
10_1101-2020_10_26_351783	126	29	,	,	,
10_1101-2020_10_26_351783	126	30	some	some	DT
10_1101-2020_10_26_351783	126	31	of	of	IN
10_1101-2020_10_26_351783	126	32	which	which	WDT
10_1101-2020_10_26_351783	126	33	were	be	VBD
10_1101-2020_10_26_351783	126	34	previously	previously	RB
10_1101-2020_10_26_351783	126	35	used	use	VBN
10_1101-2020_10_26_351783	126	36	in	in	IN
10_1101-2020_10_26_351783	126	37	the	the	DT
10_1101-2020_10_26_351783	126	38	DREAM	dream	JJ
10_1101-2020_10_26_351783	126	39	topological	topological	JJ
10_1101-2020_10_26_351783	126	40	disease	disease	NN
10_1101-2020_10_26_351783	126	41	module	module	NNP
10_1101-2020_10_26_351783	126	42	challenge[12	challenge[12	NNP
10_1101-2020_10_26_351783	126	43	]	]	-RRB-
10_1101-2020_10_26_351783	126	44	.	.	.
10_1101-2020_10_26_351783	127	1	For	for	IN
10_1101-2020_10_26_351783	127	2	these	these	DT
10_1101-2020_10_26_351783	127	3	datasets	dataset	NNS
10_1101-2020_10_26_351783	127	4	we	-PRON-	PRP
10_1101-2020_10_26_351783	127	5	assessed	assess	VBD
10_1101-2020_10_26_351783	127	6	modules	module	NNS
10_1101-2020_10_26_351783	127	7	using	use	VBG
10_1101-2020_10_26_351783	127	8	the	the	DT
10_1101-2020_10_26_351783	127	9	same	same	JJ
10_1101-2020_10_26_351783	127	10	metric	metric	NN
10_1101-2020_10_26_351783	127	11	as	as	IN
10_1101-2020_10_26_351783	127	12	in	in	IN
10_1101-2020_10_26_351783	127	13	the	the	DT
10_1101-2020_10_26_351783	127	14	recent	recent	JJ
10_1101-2020_10_26_351783	127	15	DREAM	DREAM	NNP
10_1101-2020_10_26_351783	127	16	study[12	study[12	NN
10_1101-2020_10_26_351783	127	17	]	]	-RRB-
10_1101-2020_10_26_351783	127	18	,	,	,
10_1101-2020_10_26_351783	127	19	based	base	VBN
10_1101-2020_10_26_351783	127	20	on	on	IN
10_1101-2020_10_26_351783	127	21	the	the	DT
10_1101-2020_10_26_351783	127	22	pathway	pathway	NN
10_1101-2020_10_26_351783	127	23	scoring	scoring	NN
10_1101-2020_10_26_351783	127	24	(	(	-LRB-
10_1101-2020_10_26_351783	127	25	which	which	WDT
10_1101-2020_10_26_351783	127	26	was	be	VBD
10_1101-2020_10_26_351783	127	27	not	not	RB
10_1101-2020_10_26_351783	127	28	certified	certify	VBN
10_1101-2020_10_26_351783	127	29	by	by	IN
10_1101-2020_10_26_351783	127	30	peer	peer	NN
10_1101-2020_10_26_351783	127	31	review	review	NN
10_1101-2020_10_26_351783	127	32	)	)	-RRB-
10_1101-2020_10_26_351783	127	33	is	be	VBZ
10_1101-2020_10_26_351783	127	34	the	the	DT
10_1101-2020_10_26_351783	127	35	author	author	NN
10_1101-2020_10_26_351783	127	36	/	/	SYM
10_1101-2020_10_26_351783	127	37	funder	funder	NN
10_1101-2020_10_26_351783	127	38	.	.	.
10_1101-2020_10_26_351783	128	1	All	all	DT
10_1101-2020_10_26_351783	128	2	rights	right	NNS
10_1101-2020_10_26_351783	128	3	reserved	reserve	VBD
10_1101-2020_10_26_351783	128	4	.	.	.
10_1101-2020_10_26_351783	129	1	No	no	DT
10_1101-2020_10_26_351783	129	2	reuse	reuse	NN
10_1101-2020_10_26_351783	129	3	allowed	allow	VBN
10_1101-2020_10_26_351783	129	4	without	without	IN
10_1101-2020_10_26_351783	129	5	permission	permission	NN
10_1101-2020_10_26_351783	129	6	.	.	.
10_1101-2020_10_26_351783	130	1	The	the	DT
10_1101-2020_10_26_351783	130	2	copyright	copyright	NN
10_1101-2020_10_26_351783	130	3	holder	holder	NN
10_1101-2020_10_26_351783	130	4	for	for	IN
10_1101-2020_10_26_351783	130	5	this	this	DT
10_1101-2020_10_26_351783	130	6	preprintthis	preprintthis	NN
10_1101-2020_10_26_351783	130	7	version	version	NN
10_1101-2020_10_26_351783	130	8	posted	post	VBD
10_1101-2020_10_26_351783	130	9	January	January	NNP
10_1101-2020_10_26_351783	130	10	6	6	CD
10_1101-2020_10_26_351783	130	11	,	,	,
10_1101-2020_10_26_351783	130	12	2021	2021	CD
10_1101-2020_10_26_351783	130	13	.	.	.
10_1101-2020_10_26_351783	130	14	;	;	:
10_1101-2020_10_26_351783	130	15	https://doi.org/10.1101/2020.10.26.351783doi	https://doi.org/10.1101/2020.10.26.351783doi	ADD
10_1101-2020_10_26_351783	130	16	:	:	:
10_1101-2020_10_26_351783	130	17	bioRxiv	biorxiv	IN
10_1101-2020_10_26_351783	130	18	preprint	preprint	NN
10_1101-2020_10_26_351783	130	19	https://doi.org/10.1101/2020.10.26.351783	https://doi.org/10.1101/2020.10.26.351783	CC
10_1101-2020_10_26_351783	130	20	10	10	CD
10_1101-2020_10_26_351783	130	21	algorithm	algorithm	NN
10_1101-2020_10_26_351783	130	22	(	(	-LRB-
10_1101-2020_10_26_351783	130	23	Pascal)[18	pascal)[18	NN
10_1101-2020_10_26_351783	130	24	]	]	-RRB-
10_1101-2020_10_26_351783	130	25	.	.	.
10_1101-2020_10_26_351783	131	1	For	for	IN
10_1101-2020_10_26_351783	131	2	each	each	DT
10_1101-2020_10_26_351783	131	3	disease	disease	NN
10_1101-2020_10_26_351783	131	4	we	-PRON-	PRP
10_1101-2020_10_26_351783	131	5	compiled	compile	VBD
10_1101-2020_10_26_351783	131	6	one	one	CD
10_1101-2020_10_26_351783	131	7	to	to	TO
10_1101-2020_10_26_351783	131	8	five	five	CD
10_1101-2020_10_26_351783	131	9	publicly	publicly	RB
10_1101-2020_10_26_351783	131	10	available	available	JJ
10_1101-2020_10_26_351783	131	11	transcriptomic	transcriptomic	JJ
10_1101-2020_10_26_351783	131	12	datasets	dataset	NNS
10_1101-2020_10_26_351783	131	13	considering	consider	VBG
10_1101-2020_10_26_351783	131	14	both	both	CC
10_1101-2020_10_26_351783	131	15	easily	easily	RB
10_1101-2020_10_26_351783	131	16	assessable	assessable	JJ
10_1101-2020_10_26_351783	131	17	tissues	tissue	NNS
10_1101-2020_10_26_351783	131	18	(	(	-LRB-
10_1101-2020_10_26_351783	131	19	e.g.	e.g.	RB
10_1101-2020_10_26_351783	132	1	blood	blood	NN
10_1101-2020_10_26_351783	132	2	)	)	-RRB-
10_1101-2020_10_26_351783	132	3	and	and	CC
10_1101-2020_10_26_351783	132	4	target	target	VB
10_1101-2020_10_26_351783	132	5	tissues	tissue	NNS
10_1101-2020_10_26_351783	132	6	,	,	,
10_1101-2020_10_26_351783	132	7	thereby	thereby	RB
10_1101-2020_10_26_351783	132	8	covering	cover	VBG
10_1101-2020_10_26_351783	132	9	47	47	CD
10_1101-2020_10_26_351783	132	10	transcriptomic	transcriptomic	JJ
10_1101-2020_10_26_351783	132	11	datasets	dataset	NNS
10_1101-2020_10_26_351783	132	12	in	in	IN
10_1101-2020_10_26_351783	132	13	total	total	NN
10_1101-2020_10_26_351783	132	14	(	(	-LRB-
10_1101-2020_10_26_351783	132	15	Fig	fig	NN
10_1101-2020_10_26_351783	132	16	.	.	.
10_1101-2020_10_26_351783	133	1	1a	1a	LS
10_1101-2020_10_26_351783	133	2	)	)	-RRB-
10_1101-2020_10_26_351783	133	3	.	.	.
10_1101-2020_10_26_351783	134	1	Modules	module	NNS
10_1101-2020_10_26_351783	134	2	were	be	VBD
10_1101-2020_10_26_351783	134	3	created	create	VBN
10_1101-2020_10_26_351783	134	4	using	use	VBG
10_1101-2020_10_26_351783	134	5	eight	eight	CD
10_1101-2020_10_26_351783	134	6	different	different	JJ
10_1101-2020_10_26_351783	134	7	methods	method	NNS
10_1101-2020_10_26_351783	134	8	from	from	IN
10_1101-2020_10_26_351783	134	9	MODifieR[13	MODifieR[13	NNP
10_1101-2020_10_26_351783	134	10	]	]	-RRB-
10_1101-2020_10_26_351783	134	11	.	.	.
10_1101-2020_10_26_351783	135	1	In	in	IN
10_1101-2020_10_26_351783	135	2	addition	addition	NN
10_1101-2020_10_26_351783	135	3	,	,	,
10_1101-2020_10_26_351783	135	4	we	-PRON-	PRP
10_1101-2020_10_26_351783	135	5	also	also	RB
10_1101-2020_10_26_351783	135	6	tested	test	VBD
10_1101-2020_10_26_351783	135	7	if	if	IN
10_1101-2020_10_26_351783	135	8	genes	gene	NNS
10_1101-2020_10_26_351783	135	9	detected	detect	VBN
10_1101-2020_10_26_351783	135	10	by	by	IN
10_1101-2020_10_26_351783	135	11	several	several	JJ
10_1101-2020_10_26_351783	135	12	methods	method	NNS
10_1101-2020_10_26_351783	135	13	,	,	,
10_1101-2020_10_26_351783	135	14	hereafter	hereafter	RB
10_1101-2020_10_26_351783	135	15	called	call	VBN
10_1101-2020_10_26_351783	135	16	consensus	consensus	NN
10_1101-2020_10_26_351783	135	17	module	module	NNP
10_1101-2020_10_26_351783	135	18	genes	gene	NNS
10_1101-2020_10_26_351783	135	19	,	,	,
10_1101-2020_10_26_351783	135	20	had	have	VBD
10_1101-2020_10_26_351783	135	21	higher	high	JJR
10_1101-2020_10_26_351783	135	22	enrichment	enrichment	NN
10_1101-2020_10_26_351783	135	23	scores	score	NNS
10_1101-2020_10_26_351783	135	24	than	than	IN
10_1101-2020_10_26_351783	135	25	single	single	JJ
10_1101-2020_10_26_351783	135	26	-	-	HYPH
10_1101-2020_10_26_351783	135	27	method	method	NN
10_1101-2020_10_26_351783	135	28	module	module	JJ
10_1101-2020_10_26_351783	135	29	genes	gene	NNS
10_1101-2020_10_26_351783	135	30	.	.	.
10_1101-2020_10_26_351783	136	1	Enrichment	enrichment	NN
10_1101-2020_10_26_351783	136	2	scores	score	NNS
10_1101-2020_10_26_351783	136	3	for	for	IN
10_1101-2020_10_26_351783	136	4	the	the	DT
10_1101-2020_10_26_351783	136	5	non	non	JJ
10_1101-2020_10_26_351783	136	6	-	-	JJ
10_1101-2020_10_26_351783	136	7	empty	empty	JJ
10_1101-2020_10_26_351783	136	8	modules	module	NNS
10_1101-2020_10_26_351783	136	9	(	(	-LRB-
10_1101-2020_10_26_351783	136	10	n	n	NNP
10_1101-2020_10_26_351783	136	11	=	=	SYM
10_1101-2020_10_26_351783	136	12	337	337	CD
10_1101-2020_10_26_351783	136	13	)	)	-RRB-
10_1101-2020_10_26_351783	136	14	from	from	IN
10_1101-2020_10_26_351783	136	15	this	this	DT
10_1101-2020_10_26_351783	136	16	analysis	analysis	NN
10_1101-2020_10_26_351783	136	17	were	be	VBD
10_1101-2020_10_26_351783	136	18	summarized	summarize	VBN
10_1101-2020_10_26_351783	136	19	for	for	IN
10_1101-2020_10_26_351783	136	20	each	each	DT
10_1101-2020_10_26_351783	136	21	method	method	NN
10_1101-2020_10_26_351783	136	22	and	and	CC
10_1101-2020_10_26_351783	136	23	dataset	dataset	NN
10_1101-2020_10_26_351783	136	24	(	(	-LRB-
10_1101-2020_10_26_351783	136	25	Fig	fig	NN
10_1101-2020_10_26_351783	136	26	.	.	.
10_1101-2020_10_26_351783	137	1	2a	2a	LS
10_1101-2020_10_26_351783	137	2	)	)	-RRB-
10_1101-2020_10_26_351783	137	3	.	.	.
10_1101-2020_10_26_351783	138	1	In	in	IN
10_1101-2020_10_26_351783	138	2	total	total	NN
10_1101-2020_10_26_351783	138	3	,	,	,
10_1101-2020_10_26_351783	138	4	we	-PRON-	PRP
10_1101-2020_10_26_351783	138	5	found	find	VBD
10_1101-2020_10_26_351783	138	6	significantly	significantly	RB
10_1101-2020_10_26_351783	138	7	GWAS	gwa	VBN
10_1101-2020_10_26_351783	138	8	-	-	HYPH
10_1101-2020_10_26_351783	138	9	enriched	enrich	VBN
10_1101-2020_10_26_351783	138	10	modules	module	NNS
10_1101-2020_10_26_351783	138	11	in	in	IN
10_1101-2020_10_26_351783	138	12	17.8	17.8	CD
10_1101-2020_10_26_351783	138	13	%	%	NN
10_1101-2020_10_26_351783	138	14	(	(	-LRB-
10_1101-2020_10_26_351783	138	15	60/337	60/337	CD
10_1101-2020_10_26_351783	138	16	)	)	-RRB-
10_1101-2020_10_26_351783	138	17	of	of	IN
10_1101-2020_10_26_351783	138	18	the	the	DT
10_1101-2020_10_26_351783	138	19	single	single	JJ
10_1101-2020_10_26_351783	138	20	-	-	HYPH
10_1101-2020_10_26_351783	138	21	method	method	NN
10_1101-2020_10_26_351783	138	22	modules	module	NNS
10_1101-2020_10_26_351783	138	23	and	and	CC
10_1101-2020_10_26_351783	138	24	25.5	25.5	CD
10_1101-2020_10_26_351783	138	25	%	%	NN
10_1101-2020_10_26_351783	138	26	(	(	-LRB-
10_1101-2020_10_26_351783	138	27	12/47	12/47	CD
10_1101-2020_10_26_351783	138	28	)	)	-RRB-
10_1101-2020_10_26_351783	138	29	of	of	IN
10_1101-2020_10_26_351783	138	30	the	the	DT
10_1101-2020_10_26_351783	138	31	non	non	JJ
10_1101-2020_10_26_351783	138	32	-	-	JJ
10_1101-2020_10_26_351783	138	33	empty	empty	JJ
10_1101-2020_10_26_351783	138	34	consensus	consensus	NN
10_1101-2020_10_26_351783	138	35	modules	module	NNS
10_1101-2020_10_26_351783	138	36	that	that	IN
10_1101-2020_10_26_351783	138	37	combined	combine	VBD
10_1101-2020_10_26_351783	138	38	at	at	RB
10_1101-2020_10_26_351783	138	39	least	least	RBS
10_1101-2020_10_26_351783	138	40	three	three	CD
10_1101-2020_10_26_351783	138	41	methods	method	NNS
10_1101-2020_10_26_351783	138	42	as	as	IN
10_1101-2020_10_26_351783	138	43	a	a	DT
10_1101-2020_10_26_351783	138	44	criterion	criterion	NN
10_1101-2020_10_26_351783	138	45	.	.	.
10_1101-2020_10_26_351783	139	1	These	these	DT
10_1101-2020_10_26_351783	139	2	numbers	number	NNS
10_1101-2020_10_26_351783	139	3	seemed	seem	VBD
10_1101-2020_10_26_351783	139	4	higher	high	JJR
10_1101-2020_10_26_351783	139	5	than	than	IN
10_1101-2020_10_26_351783	139	6	expected	expect	VBN
10_1101-2020_10_26_351783	139	7	,	,	,
10_1101-2020_10_26_351783	139	8	which	which	WDT
10_1101-2020_10_26_351783	139	9	might	may	MD
10_1101-2020_10_26_351783	139	10	have	have	VB
10_1101-2020_10_26_351783	139	11	been	be	VBN
10_1101-2020_10_26_351783	139	12	a	a	DT
10_1101-2020_10_26_351783	139	13	consequence	consequence	NN
10_1101-2020_10_26_351783	139	14	of	of	IN
10_1101-2020_10_26_351783	139	15	the	the	DT
10_1101-2020_10_26_351783	139	16	same	same	JJ
10_1101-2020_10_26_351783	139	17	GWAS	GWAS	NNP
10_1101-2020_10_26_351783	139	18	being	be	VBG
10_1101-2020_10_26_351783	139	19	used	use	VBN
10_1101-2020_10_26_351783	139	20	to	to	TO
10_1101-2020_10_26_351783	139	21	evaluate	evaluate	VB
10_1101-2020_10_26_351783	139	22	multiple	multiple	JJ
10_1101-2020_10_26_351783	139	23	transcriptomic	transcriptomic	JJ
10_1101-2020_10_26_351783	139	24	datasets	dataset	NNS
10_1101-2020_10_26_351783	139	25	of	of	IN
10_1101-2020_10_26_351783	139	26	the	the	DT
10_1101-2020_10_26_351783	139	27	same	same	JJ
10_1101-2020_10_26_351783	139	28	disease	disease	NN
10_1101-2020_10_26_351783	139	29	.	.	.
10_1101-2020_10_26_351783	140	1	Hence	hence	RB
10_1101-2020_10_26_351783	140	2	,	,	,
10_1101-2020_10_26_351783	140	3	we	-PRON-	PRP
10_1101-2020_10_26_351783	140	4	aggregated	aggregate	VBD
10_1101-2020_10_26_351783	140	5	scores	score	NNS
10_1101-2020_10_26_351783	140	6	of	of	IN
10_1101-2020_10_26_351783	140	7	the	the	DT
10_1101-2020_10_26_351783	140	8	same	same	JJ
10_1101-2020_10_26_351783	140	9	disease	disease	NN
10_1101-2020_10_26_351783	140	10	and	and	CC
10_1101-2020_10_26_351783	140	11	method	method	NN
10_1101-2020_10_26_351783	140	12	as	as	IN
10_1101-2020_10_26_351783	140	13	meta	meta	JJ
10_1101-2020_10_26_351783	140	14	P	p	NN
10_1101-2020_10_26_351783	140	15	-	-	HYPH
10_1101-2020_10_26_351783	140	16	values	value	NNS
10_1101-2020_10_26_351783	140	17	(	(	-LRB-
10_1101-2020_10_26_351783	140	18	see	see	VB
10_1101-2020_10_26_351783	140	19	Methods	method	NNS
10_1101-2020_10_26_351783	140	20	)	)	-RRB-
10_1101-2020_10_26_351783	140	21	.	.	.
10_1101-2020_10_26_351783	141	1	Out	out	IN
10_1101-2020_10_26_351783	141	2	of	of	IN
10_1101-2020_10_26_351783	141	3	the	the	DT
10_1101-2020_10_26_351783	141	4	152	152	CD
10_1101-2020_10_26_351783	141	5	possible	possible	JJ
10_1101-2020_10_26_351783	141	6	disease	disease	NN
10_1101-2020_10_26_351783	141	7	-	-	HYPH
10_1101-2020_10_26_351783	141	8	method	method	NN
10_1101-2020_10_26_351783	141	9	combinations	combination	NNS
10_1101-2020_10_26_351783	141	10	,	,	,
10_1101-2020_10_26_351783	141	11	18	18	CD
10_1101-2020_10_26_351783	141	12	%	%	NN
10_1101-2020_10_26_351783	141	13	of	of	IN
10_1101-2020_10_26_351783	141	14	the	the	DT
10_1101-2020_10_26_351783	141	15	pairs	pair	NNS
10_1101-2020_10_26_351783	141	16	showed	show	VBD
10_1101-2020_10_26_351783	141	17	a	a	DT
10_1101-2020_10_26_351783	141	18	significant	significant	JJ
10_1101-2020_10_26_351783	141	19	GWAS	GWAS	NNP
10_1101-2020_10_26_351783	141	20	Pascal	pascal	JJ
10_1101-2020_10_26_351783	141	21	enrichment	enrichment	NN
10_1101-2020_10_26_351783	141	22	,	,	,
10_1101-2020_10_26_351783	141	23	which	which	WDT
10_1101-2020_10_26_351783	141	24	is	be	VBZ
10_1101-2020_10_26_351783	141	25	more	more	JJR
10_1101-2020_10_26_351783	141	26	than	than	IN
10_1101-2020_10_26_351783	141	27	expected	expect	VBN
10_1101-2020_10_26_351783	141	28	by	by	IN
10_1101-2020_10_26_351783	141	29	chance	chance	NN
10_1101-2020_10_26_351783	141	30	(	(	-LRB-
10_1101-2020_10_26_351783	141	31	n	n	NN
10_1101-2020_10_26_351783	141	32	=	=	SYM
10_1101-2020_10_26_351783	141	33	27	27	CD
10_1101-2020_10_26_351783	141	34	,	,	,
10_1101-2020_10_26_351783	141	35	P	p	NN
10_1101-2020_10_26_351783	141	36	=	=	SYM
10_1101-2020_10_26_351783	141	37	1.0	1.0	CD
10_1101-2020_10_26_351783	141	38	x	x	SYM
10_1101-2020_10_26_351783	141	39	10	10	CD
10_1101-2020_10_26_351783	141	40	-	-	SYM
10_1101-2020_10_26_351783	141	41	8	8	CD
10_1101-2020_10_26_351783	141	42	)	)	-RRB-
10_1101-2020_10_26_351783	141	43	.	.	.
10_1101-2020_10_26_351783	142	1	The	the	DT
10_1101-2020_10_26_351783	142	2	most	most	RBS
10_1101-2020_10_26_351783	142	3	enriched	enriched	JJ
10_1101-2020_10_26_351783	142	4	method	method	NN
10_1101-2020_10_26_351783	142	5	was	be	VBD
10_1101-2020_10_26_351783	142	6	Clique	Clique	NNP
10_1101-2020_10_26_351783	142	7	SuM	sum	NN
10_1101-2020_10_26_351783	142	8	,	,	,
10_1101-2020_10_26_351783	142	9	which	which	WDT
10_1101-2020_10_26_351783	142	10	showed	show	VBD
10_1101-2020_10_26_351783	142	11	significant	significant	JJ
10_1101-2020_10_26_351783	142	12	enrichment	enrichment	NN
10_1101-2020_10_26_351783	142	13	in	in	IN
10_1101-2020_10_26_351783	142	14	seven	seven	CD
10_1101-2020_10_26_351783	142	15	out	out	IN
10_1101-2020_10_26_351783	142	16	of	of	IN
10_1101-2020_10_26_351783	142	17	19	19	CD
10_1101-2020_10_26_351783	142	18	diseases	disease	NNS
10_1101-2020_10_26_351783	142	19	(	(	-LRB-
10_1101-2020_10_26_351783	142	20	binomial	binomial	JJ
10_1101-2020_10_26_351783	142	21	test	test	NN
10_1101-2020_10_26_351783	142	22	P	p	NN
10_1101-2020_10_26_351783	142	23	=	=	SYM
10_1101-2020_10_26_351783	142	24	2.3	2.3	CD
10_1101-2020_10_26_351783	142	25	x	x	SYM
10_1101-2020_10_26_351783	142	26	10	10	CD
10_1101-2020_10_26_351783	142	27	-	-	SYM
10_1101-2020_10_26_351783	142	28	5	5	CD
10_1101-2020_10_26_351783	142	29	)	)	-RRB-
10_1101-2020_10_26_351783	142	30	.	.	.
10_1101-2020_10_26_351783	143	1	Many	many	JJ
10_1101-2020_10_26_351783	143	2	methods	method	NNS
10_1101-2020_10_26_351783	143	3	exhibited	exhibit	VBD
10_1101-2020_10_26_351783	143	4	strong	strong	JJ
10_1101-2020_10_26_351783	143	5	enrichments	enrichment	NNS
10_1101-2020_10_26_351783	143	6	in	in	IN
10_1101-2020_10_26_351783	143	7	coronary	coronary	JJ
10_1101-2020_10_26_351783	143	8	artery	artery	NN
10_1101-2020_10_26_351783	143	9	disease	disease	NN
10_1101-2020_10_26_351783	143	10	(	(	-LRB-
10_1101-2020_10_26_351783	143	11	CAD	CAD	NNP
10_1101-2020_10_26_351783	143	12	)	)	-RRB-
10_1101-2020_10_26_351783	143	13	,	,	,
10_1101-2020_10_26_351783	143	14	type	type	NN
10_1101-2020_10_26_351783	143	15	2	2	CD
10_1101-2020_10_26_351783	143	16	diabetes	diabetes	NN
10_1101-2020_10_26_351783	143	17	,	,	,
10_1101-2020_10_26_351783	143	18	multiple	multiple	JJ
10_1101-2020_10_26_351783	143	19	sclerosis	sclerosis	NN
10_1101-2020_10_26_351783	143	20	(	(	-LRB-
10_1101-2020_10_26_351783	143	21	MS	MS	NNP
10_1101-2020_10_26_351783	143	22	)	)	-RRB-
10_1101-2020_10_26_351783	143	23	,	,	,
10_1101-2020_10_26_351783	143	24	rheumatoid	rheumatoid	NNP
10_1101-2020_10_26_351783	143	25	arthritis	arthritis	NN
10_1101-2020_10_26_351783	143	26	(	(	-LRB-
10_1101-2020_10_26_351783	143	27	RA	RA	NNP
10_1101-2020_10_26_351783	143	28	)	)	-RRB-
10_1101-2020_10_26_351783	143	29	,	,	,
10_1101-2020_10_26_351783	143	30	and	and	CC
10_1101-2020_10_26_351783	143	31	the	the	DT
10_1101-2020_10_26_351783	143	32	inflammatory	inflammatory	JJ
10_1101-2020_10_26_351783	143	33	bowel	bowel	NN
10_1101-2020_10_26_351783	143	34	diseases(IBD	diseases(IBD	NNP
10_1101-2020_10_26_351783	143	35	)	)	-RRB-
10_1101-2020_10_26_351783	143	36	,	,	,
10_1101-2020_10_26_351783	143	37	ulcerative	ulcerative	JJ
10_1101-2020_10_26_351783	143	38	colitis	colitis	NN
10_1101-2020_10_26_351783	143	39	(	(	-LRB-
10_1101-2020_10_26_351783	143	40	UC	UC	NNP
10_1101-2020_10_26_351783	143	41	)	)	-RRB-
10_1101-2020_10_26_351783	143	42	and	and	CC
10_1101-2020_10_26_351783	143	43	Crohn	Crohn	NNP
10_1101-2020_10_26_351783	143	44	’s	’s	POS
10_1101-2020_10_26_351783	143	45	disease	disease	NN
10_1101-2020_10_26_351783	143	46	(	(	-LRB-
10_1101-2020_10_26_351783	143	47	CD	cd	NN
10_1101-2020_10_26_351783	143	48	)	)	-RRB-
10_1101-2020_10_26_351783	143	49	,	,	,
10_1101-2020_10_26_351783	143	50	while	while	IN
10_1101-2020_10_26_351783	143	51	no	no	DT
10_1101-2020_10_26_351783	143	52	significant	significant	JJ
10_1101-2020_10_26_351783	143	53	enrichments	enrichment	NNS
10_1101-2020_10_26_351783	143	54	were	be	VBD
10_1101-2020_10_26_351783	143	55	found	find	VBN
10_1101-2020_10_26_351783	143	56	for	for	IN
10_1101-2020_10_26_351783	143	57	asthma	asthma	NN
10_1101-2020_10_26_351783	143	58	,	,	,
10_1101-2020_10_26_351783	143	59	hepatitis	hepatitis	NNP
10_1101-2020_10_26_351783	143	60	C	C	NNP
10_1101-2020_10_26_351783	143	61	,	,	,
10_1101-2020_10_26_351783	143	62	type	type	NN
10_1101-2020_10_26_351783	143	63	1	1	CD
10_1101-2020_10_26_351783	143	64	diabetes	diabetes	NN
10_1101-2020_10_26_351783	143	65	,	,	,
10_1101-2020_10_26_351783	143	66	narcolepsy	narcolepsy	NN
10_1101-2020_10_26_351783	143	67	,	,	,
10_1101-2020_10_26_351783	143	68	Parkinson	Parkinson	NNP
10_1101-2020_10_26_351783	143	69	’s	’s	POS
10_1101-2020_10_26_351783	143	70	disease	disease	NN
10_1101-2020_10_26_351783	143	71	,	,	,
10_1101-2020_10_26_351783	143	72	or	or	CC
10_1101-2020_10_26_351783	143	73	for	for	IN
10_1101-2020_10_26_351783	143	74	any	any	DT
10_1101-2020_10_26_351783	143	75	psychiatric	psychiatric	JJ
10_1101-2020_10_26_351783	143	76	and	and	CC
10_1101-2020_10_26_351783	143	77	social	social	JJ
10_1101-2020_10_26_351783	143	78	diseases	disease	NNS
10_1101-2020_10_26_351783	143	79	.	.	.
10_1101-2020_10_26_351783	144	1	If	if	IN
10_1101-2020_10_26_351783	144	2	we	-PRON-	PRP
10_1101-2020_10_26_351783	144	3	instead	instead	RB
10_1101-2020_10_26_351783	144	4	ranked	rank	VBD
10_1101-2020_10_26_351783	144	5	methods	method	NNS
10_1101-2020_10_26_351783	144	6	based	base	VBN
10_1101-2020_10_26_351783	144	7	on	on	IN
10_1101-2020_10_26_351783	144	8	their	-PRON-	PRP$
10_1101-2020_10_26_351783	144	9	respective	respective	JJ
10_1101-2020_10_26_351783	144	10	module	module	NN
10_1101-2020_10_26_351783	144	11	GWAS	gwa	VBN
10_1101-2020_10_26_351783	144	12	enrichment	enrichment	NN
10_1101-2020_10_26_351783	144	13	,	,	,
10_1101-2020_10_26_351783	144	14	Clique	Clique	NNP
10_1101-2020_10_26_351783	144	15	SuM	sum	NN
10_1101-2020_10_26_351783	144	16	showed	show	VBD
10_1101-2020_10_26_351783	144	17	significant	significant	JJ
10_1101-2020_10_26_351783	144	18	association	association	NN
10_1101-2020_10_26_351783	144	19	in	in	IN
10_1101-2020_10_26_351783	144	20	34	34	CD
10_1101-2020_10_26_351783	144	21	%	%	NN
10_1101-2020_10_26_351783	144	22	(	(	-LRB-
10_1101-2020_10_26_351783	144	23	16/47	16/47	CD
10_1101-2020_10_26_351783	144	24	)	)	-RRB-
10_1101-2020_10_26_351783	144	25	of	of	IN
10_1101-2020_10_26_351783	144	26	the	the	DT
10_1101-2020_10_26_351783	144	27	modules	module	NNS
10_1101-2020_10_26_351783	144	28	corresponding	correspond	VBG
10_1101-2020_10_26_351783	144	29	to	to	IN
10_1101-2020_10_26_351783	144	30	seven	seven	CD
10_1101-2020_10_26_351783	144	31	different	different	JJ
10_1101-2020_10_26_351783	144	32	diseases	disease	NNS
10_1101-2020_10_26_351783	144	33	followed	follow	VBN
10_1101-2020_10_26_351783	144	34	by	by	IN
10_1101-2020_10_26_351783	144	35	consensus	consensus	NN
10_1101-2020_10_26_351783	144	36	modules	module	NNS
10_1101-2020_10_26_351783	144	37	identified	identify	VBN
10_1101-2020_10_26_351783	144	38	by	by	IN
10_1101-2020_10_26_351783	144	39	two	two	CD
10_1101-2020_10_26_351783	144	40	out	out	IN
10_1101-2020_10_26_351783	144	41	of	of	IN
10_1101-2020_10_26_351783	144	42	three	three	CD
10_1101-2020_10_26_351783	144	43	methods	method	NNS
10_1101-2020_10_26_351783	144	44	.	.	.
10_1101-2020_10_26_351783	145	1	Lastly	lastly	RB
10_1101-2020_10_26_351783	145	2	,	,	,
10_1101-2020_10_26_351783	145	3	DIAMOnD	diamond	NN
10_1101-2020_10_26_351783	145	4	and	and	CC
10_1101-2020_10_26_351783	145	5	co-	co-	VBD
10_1101-2020_10_26_351783	145	6	expression	expression	NN
10_1101-2020_10_26_351783	145	7	-	-	HYPH
10_1101-2020_10_26_351783	145	8	based	base	VBN
10_1101-2020_10_26_351783	145	9	methods	method	NNS
10_1101-2020_10_26_351783	145	10	all	all	DT
10_1101-2020_10_26_351783	145	11	achieved	achieve	VBN
10_1101-2020_10_26_351783	145	12	significant	significant	JJ
10_1101-2020_10_26_351783	145	13	results	result	NNS
10_1101-2020_10_26_351783	145	14	,	,	,
10_1101-2020_10_26_351783	145	15	although	although	IN
10_1101-2020_10_26_351783	145	16	worse	bad	JJR
10_1101-2020_10_26_351783	145	17	than	than	IN
10_1101-2020_10_26_351783	145	18	Clique	Clique	NNP
10_1101-2020_10_26_351783	145	19	SuM.	SuM.	NNP
10_1101-2020_10_26_351783	146	1	Next	next	RB
10_1101-2020_10_26_351783	146	2	,	,	,
10_1101-2020_10_26_351783	146	3	we	-PRON-	PRP
10_1101-2020_10_26_351783	146	4	tested	test	VBD
10_1101-2020_10_26_351783	146	5	the	the	DT
10_1101-2020_10_26_351783	146	6	impact	impact	NN
10_1101-2020_10_26_351783	146	7	of	of	IN
10_1101-2020_10_26_351783	146	8	network	network	NN
10_1101-2020_10_26_351783	146	9	centrality	centrality	NN
10_1101-2020_10_26_351783	146	10	and	and	CC
10_1101-2020_10_26_351783	146	11	module	module	JJ
10_1101-2020_10_26_351783	146	12	size	size	NN
10_1101-2020_10_26_351783	146	13	as	as	IN
10_1101-2020_10_26_351783	146	14	potential	potential	JJ
10_1101-2020_10_26_351783	146	15	confounding	confound	VBG
10_1101-2020_10_26_351783	146	16	factors	factor	NNS
10_1101-2020_10_26_351783	146	17	of	of	IN
10_1101-2020_10_26_351783	146	18	the	the	DT
10_1101-2020_10_26_351783	146	19	applied	applied	JJ
10_1101-2020_10_26_351783	146	20	performance	performance	NN
10_1101-2020_10_26_351783	146	21	metric	metric	NN
10_1101-2020_10_26_351783	146	22	.	.	.
10_1101-2020_10_26_351783	147	1	We	-PRON-	PRP
10_1101-2020_10_26_351783	147	2	found	find	VBD
10_1101-2020_10_26_351783	147	3	a	a	DT
10_1101-2020_10_26_351783	147	4	significant	significant	JJ
10_1101-2020_10_26_351783	147	5	but	but	CC
10_1101-2020_10_26_351783	147	6	very	very	RB
10_1101-2020_10_26_351783	147	7	modest	modest	JJ
10_1101-2020_10_26_351783	147	8	correlation	correlation	NN
10_1101-2020_10_26_351783	147	9	for	for	IN
10_1101-2020_10_26_351783	147	10	module	module	JJ
10_1101-2020_10_26_351783	147	11	size	size	NN
10_1101-2020_10_26_351783	147	12	(	(	-LRB-
10_1101-2020_10_26_351783	147	13	Fig	fig	NN
10_1101-2020_10_26_351783	147	14	.	.	.
10_1101-2020_10_26_351783	148	1	2c	2c	LS
10_1101-2020_10_26_351783	148	2	,	,	,
10_1101-2020_10_26_351783	148	3	Spearman	Spearman	NNP
10_1101-2020_10_26_351783	148	4	rho	rho	NN
10_1101-2020_10_26_351783	148	5	=	=	SYM
10_1101-2020_10_26_351783	148	6	0.165	0.165	CD
10_1101-2020_10_26_351783	148	7	,	,	,
10_1101-2020_10_26_351783	148	8	P	p	NN
10_1101-2020_10_26_351783	148	9	=	=	SYM
10_1101-2020_10_26_351783	148	10	2.3	2.3	CD
10_1101-2020_10_26_351783	148	11	x	x	SYM
10_1101-2020_10_26_351783	148	12	10	10	CD
10_1101-2020_10_26_351783	148	13	-	-	SYM
10_1101-2020_10_26_351783	148	14	3	3	CD
10_1101-2020_10_26_351783	148	15	)	)	-RRB-
10_1101-2020_10_26_351783	148	16	,	,	,
10_1101-2020_10_26_351783	148	17	and	and	CC
10_1101-2020_10_26_351783	148	18	a	a	DT
10_1101-2020_10_26_351783	148	19	non	non	JJ
10_1101-2020_10_26_351783	148	20	-	-	JJ
10_1101-2020_10_26_351783	148	21	significant	significant	JJ
10_1101-2020_10_26_351783	148	22	correlation	correlation	NN
10_1101-2020_10_26_351783	148	23	for	for	IN
10_1101-2020_10_26_351783	148	24	interactome	interactome	NN
10_1101-2020_10_26_351783	148	25	(	(	-LRB-
10_1101-2020_10_26_351783	148	26	which	which	WDT
10_1101-2020_10_26_351783	148	27	was	be	VBD
10_1101-2020_10_26_351783	148	28	not	not	RB
10_1101-2020_10_26_351783	148	29	certified	certify	VBN
10_1101-2020_10_26_351783	148	30	by	by	IN
10_1101-2020_10_26_351783	148	31	peer	peer	NN
10_1101-2020_10_26_351783	148	32	review	review	NN
10_1101-2020_10_26_351783	148	33	)	)	-RRB-
10_1101-2020_10_26_351783	148	34	is	be	VBZ
10_1101-2020_10_26_351783	148	35	the	the	DT
10_1101-2020_10_26_351783	148	36	author	author	NN
10_1101-2020_10_26_351783	148	37	/	/	SYM
10_1101-2020_10_26_351783	148	38	funder	funder	NN
10_1101-2020_10_26_351783	148	39	.	.	.
10_1101-2020_10_26_351783	149	1	All	all	DT
10_1101-2020_10_26_351783	149	2	rights	right	NNS
10_1101-2020_10_26_351783	149	3	reserved	reserve	VBD
10_1101-2020_10_26_351783	149	4	.	.	.
10_1101-2020_10_26_351783	150	1	No	no	DT
10_1101-2020_10_26_351783	150	2	reuse	reuse	NN
10_1101-2020_10_26_351783	150	3	allowed	allow	VBN
10_1101-2020_10_26_351783	150	4	without	without	IN
10_1101-2020_10_26_351783	150	5	permission	permission	NN
10_1101-2020_10_26_351783	150	6	.	.	.
10_1101-2020_10_26_351783	151	1	The	the	DT
10_1101-2020_10_26_351783	151	2	copyright	copyright	NN
10_1101-2020_10_26_351783	151	3	holder	holder	NN
10_1101-2020_10_26_351783	151	4	for	for	IN
10_1101-2020_10_26_351783	151	5	this	this	DT
10_1101-2020_10_26_351783	151	6	preprintthis	preprintthis	NN
10_1101-2020_10_26_351783	151	7	version	version	NN
10_1101-2020_10_26_351783	151	8	posted	post	VBD
10_1101-2020_10_26_351783	151	9	January	January	NNP
10_1101-2020_10_26_351783	151	10	6	6	CD
10_1101-2020_10_26_351783	151	11	,	,	,
10_1101-2020_10_26_351783	151	12	2021	2021	CD
10_1101-2020_10_26_351783	151	13	.	.	.
10_1101-2020_10_26_351783	151	14	;	;	:
10_1101-2020_10_26_351783	151	15	https://doi.org/10.1101/2020.10.26.351783doi	https://doi.org/10.1101/2020.10.26.351783doi	ADD
10_1101-2020_10_26_351783	151	16	:	:	:
10_1101-2020_10_26_351783	151	17	bioRxiv	biorxiv	VB
10_1101-2020_10_26_351783	151	18	preprint	preprint	NN
10_1101-2020_10_26_351783	151	19	https://doi.org/10.1101/2020.10.26.351783	https://doi.org/10.1101/2020.10.26.351783	CC
10_1101-2020_10_26_351783	151	20	11	11	CD
10_1101-2020_10_26_351783	151	21	centrality	centrality	NN
10_1101-2020_10_26_351783	151	22	(	(	-LRB-
10_1101-2020_10_26_351783	151	23	Fig	fig	NN
10_1101-2020_10_26_351783	151	24	.	.	.
10_1101-2020_10_26_351783	152	1	2b	2b	NNP
10_1101-2020_10_26_351783	152	2	,	,	,
10_1101-2020_10_26_351783	152	3	rho	rho	NNP
10_1101-2020_10_26_351783	152	4	=	=	SYM
10_1101-2020_10_26_351783	152	5	0.068	0.068	CD
10_1101-2020_10_26_351783	152	6	,	,	,
10_1101-2020_10_26_351783	152	7	P	p	NN
10_1101-2020_10_26_351783	152	8	=	=	SYM
10_1101-2020_10_26_351783	152	9	0.21	0.21	CD
10_1101-2020_10_26_351783	152	10	)	)	-RRB-
10_1101-2020_10_26_351783	152	11	.	.	.
10_1101-2020_10_26_351783	153	1	Thus	thus	RB
10_1101-2020_10_26_351783	153	2	,	,	,
10_1101-2020_10_26_351783	153	3	it	-PRON-	PRP
10_1101-2020_10_26_351783	153	4	is	be	VBZ
10_1101-2020_10_26_351783	153	5	meaningful	meaningful	JJ
10_1101-2020_10_26_351783	153	6	to	to	TO
10_1101-2020_10_26_351783	153	7	compare	compare	VB
10_1101-2020_10_26_351783	153	8	results	result	NNS
10_1101-2020_10_26_351783	153	9	with	with	IN
10_1101-2020_10_26_351783	153	10	differences	difference	NNS
10_1101-2020_10_26_351783	153	11	in	in	IN
10_1101-2020_10_26_351783	153	12	those	those	DT
10_1101-2020_10_26_351783	153	13	module	module	JJ
10_1101-2020_10_26_351783	153	14	properties	property	NNS
10_1101-2020_10_26_351783	153	15	.	.	.
10_1101-2020_10_26_351783	154	1	In	in	IN
10_1101-2020_10_26_351783	154	2	summary	summary	NN
10_1101-2020_10_26_351783	154	3	,	,	,
10_1101-2020_10_26_351783	154	4	we	-PRON-	PRP
10_1101-2020_10_26_351783	154	5	found	find	VBD
10_1101-2020_10_26_351783	154	6	that	that	IN
10_1101-2020_10_26_351783	154	7	the	the	DT
10_1101-2020_10_26_351783	154	8	Clique	Clique	NNP
10_1101-2020_10_26_351783	154	9	SuM	sum	NN
10_1101-2020_10_26_351783	154	10	method	method	NN
10_1101-2020_10_26_351783	154	11	resulted	result	VBD
10_1101-2020_10_26_351783	154	12	in	in	IN
10_1101-2020_10_26_351783	154	13	the	the	DT
10_1101-2020_10_26_351783	154	14	highest	high	JJS
10_1101-2020_10_26_351783	154	15	disease	disease	NN
10_1101-2020_10_26_351783	154	16	enrichment	enrichment	NN
10_1101-2020_10_26_351783	154	17	for	for	IN
10_1101-2020_10_26_351783	154	18	most	most	JJS
10_1101-2020_10_26_351783	154	19	diseases	disease	NNS
10_1101-2020_10_26_351783	154	20	,	,	,
10_1101-2020_10_26_351783	154	21	while	while	IN
10_1101-2020_10_26_351783	154	22	not	not	RB
10_1101-2020_10_26_351783	154	23	producing	produce	VBG
10_1101-2020_10_26_351783	154	24	significant	significant	JJ
10_1101-2020_10_26_351783	154	25	modules	module	NNS
10_1101-2020_10_26_351783	154	26	for	for	IN
10_1101-2020_10_26_351783	154	27	others	other	NNS
10_1101-2020_10_26_351783	154	28	,	,	,
10_1101-2020_10_26_351783	154	29	such	such	JJ
10_1101-2020_10_26_351783	154	30	as	as	IN
10_1101-2020_10_26_351783	154	31	type	type	NN
10_1101-2020_10_26_351783	154	32	2	2	CD
10_1101-2020_10_26_351783	154	33	diabetes	diabetes	NN
10_1101-2020_10_26_351783	154	34	,	,	,
10_1101-2020_10_26_351783	154	35	where	where	WRB
10_1101-2020_10_26_351783	154	36	co	co	NN
10_1101-2020_10_26_351783	154	37	-	-	JJ
10_1101-2020_10_26_351783	154	38	expression	expression	NN
10_1101-2020_10_26_351783	154	39	-	-	HYPH
10_1101-2020_10_26_351783	154	40	based	base	VBN
10_1101-2020_10_26_351783	154	41	methods	method	NNS
10_1101-2020_10_26_351783	154	42	and	and	CC
10_1101-2020_10_26_351783	154	43	DIAMOnD	diamond	NN
10_1101-2020_10_26_351783	154	44	scored	score	VBD
10_1101-2020_10_26_351783	154	45	best	well	RBS
10_1101-2020_10_26_351783	154	46	.	.	.
10_1101-2020_10_26_351783	155	1	In	in	IN
10_1101-2020_10_26_351783	155	2	general	general	JJ
10_1101-2020_10_26_351783	155	3	,	,	,
10_1101-2020_10_26_351783	155	4	we	-PRON-	PRP
10_1101-2020_10_26_351783	155	5	observed	observe	VBD
10_1101-2020_10_26_351783	155	6	stronger	strong	JJR
10_1101-2020_10_26_351783	155	7	enrichments	enrichment	NNS
10_1101-2020_10_26_351783	155	8	for	for	IN
10_1101-2020_10_26_351783	155	9	inflammatory	inflammatory	JJ
10_1101-2020_10_26_351783	155	10	diseases	disease	NNS
10_1101-2020_10_26_351783	155	11	and	and	CC
10_1101-2020_10_26_351783	155	12	weaker	weak	JJR
10_1101-2020_10_26_351783	155	13	results	result	NNS
10_1101-2020_10_26_351783	155	14	for	for	IN
10_1101-2020_10_26_351783	155	15	psychiatric	psychiatric	JJ
10_1101-2020_10_26_351783	155	16	and	and	CC
10_1101-2020_10_26_351783	155	17	social	social	JJ
10_1101-2020_10_26_351783	155	18	diseases	disease	NNS
10_1101-2020_10_26_351783	155	19	.	.	.
10_1101-2020_10_26_351783	156	1	Considering	consider	VBG
10_1101-2020_10_26_351783	156	2	that	that	IN
10_1101-2020_10_26_351783	156	3	the	the	DT
10_1101-2020_10_26_351783	156	4	transcriptomic	transcriptomic	JJ
10_1101-2020_10_26_351783	156	5	modules	module	NNS
10_1101-2020_10_26_351783	156	6	showed	show	VBD
10_1101-2020_10_26_351783	156	7	that	that	IN
10_1101-2020_10_26_351783	156	8	Clique	Clique	NNP
10_1101-2020_10_26_351783	156	9	SuM	sum	NN
10_1101-2020_10_26_351783	156	10	was	be	VBD
10_1101-2020_10_26_351783	156	11	the	the	DT
10_1101-2020_10_26_351783	156	12	best	good	JJS
10_1101-2020_10_26_351783	156	13	performing	performing	NN
10_1101-2020_10_26_351783	156	14	method	method	NN
10_1101-2020_10_26_351783	156	15	and	and	CC
10_1101-2020_10_26_351783	156	16	that	that	IN
10_1101-2020_10_26_351783	156	17	the	the	DT
10_1101-2020_10_26_351783	156	18	cardiovascular	cardiovascular	JJ
10_1101-2020_10_26_351783	156	19	and	and	CC
10_1101-2020_10_26_351783	156	20	inflammatory	inflammatory	JJ
10_1101-2020_10_26_351783	156	21	diseases	disease	NNS
10_1101-2020_10_26_351783	156	22	were	be	VBD
10_1101-2020_10_26_351783	156	23	the	the	DT
10_1101-2020_10_26_351783	156	24	most	most	RBS
10_1101-2020_10_26_351783	156	25	enriched	enrich	VBN
10_1101-2020_10_26_351783	156	26	within	within	IN
10_1101-2020_10_26_351783	156	27	the	the	DT
10_1101-2020_10_26_351783	156	28	Clique	Clique	NNP
10_1101-2020_10_26_351783	156	29	SuM	sum	NN
10_1101-2020_10_26_351783	156	30	modules	module	NNS
10_1101-2020_10_26_351783	156	31	,	,	,
10_1101-2020_10_26_351783	156	32	we	-PRON-	PRP
10_1101-2020_10_26_351783	156	33	wanted	want	VBD
10_1101-2020_10_26_351783	156	34	to	to	TO
10_1101-2020_10_26_351783	156	35	test	test	VB
10_1101-2020_10_26_351783	156	36	whether	whether	IN
10_1101-2020_10_26_351783	156	37	this	this	DT
10_1101-2020_10_26_351783	156	38	was	be	VBD
10_1101-2020_10_26_351783	156	39	true	true	JJ
10_1101-2020_10_26_351783	156	40	for	for	IN
10_1101-2020_10_26_351783	156	41	methylomic	methylomic	JJ
10_1101-2020_10_26_351783	156	42	data	datum	NNS
10_1101-2020_10_26_351783	156	43	as	as	RB
10_1101-2020_10_26_351783	156	44	well	well	RB
10_1101-2020_10_26_351783	156	45	.	.	.
10_1101-2020_10_26_351783	157	1	A	a	DT
10_1101-2020_10_26_351783	157	2	benchmark	benchmark	NN
10_1101-2020_10_26_351783	157	3	comparing	compare	VBG
10_1101-2020_10_26_351783	157	4	72	72	CD
10_1101-2020_10_26_351783	157	5	methylation	methylation	NN
10_1101-2020_10_26_351783	157	6	-	-	HYPH
10_1101-2020_10_26_351783	157	7	based	base	VBN
10_1101-2020_10_26_351783	157	8	disease	disease	NN
10_1101-2020_10_26_351783	157	9	modules	module	NNS
10_1101-2020_10_26_351783	157	10	from	from	IN
10_1101-2020_10_26_351783	157	11	six	six	CD
10_1101-2020_10_26_351783	157	12	different	different	JJ
10_1101-2020_10_26_351783	157	13	diseases	disease	NNS
10_1101-2020_10_26_351783	157	14	using	use	VBG
10_1101-2020_10_26_351783	157	15	GWAS	GWAS	NNP
10_1101-2020_10_26_351783	157	16	.	.	.
10_1101-2020_10_26_351783	158	1	Following	follow	VBG
10_1101-2020_10_26_351783	158	2	the	the	DT
10_1101-2020_10_26_351783	158	3	same	same	JJ
10_1101-2020_10_26_351783	158	4	logic	logic	NN
10_1101-2020_10_26_351783	158	5	of	of	IN
10_1101-2020_10_26_351783	158	6	the	the	DT
10_1101-2020_10_26_351783	158	7	transcriptomic	transcriptomic	NNP
10_1101-2020_10_26_351783	158	8	benchmark	benchmark	NN
10_1101-2020_10_26_351783	158	9	,	,	,
10_1101-2020_10_26_351783	158	10	we	-PRON-	PRP
10_1101-2020_10_26_351783	158	11	performed	perform	VBD
10_1101-2020_10_26_351783	158	12	a	a	DT
10_1101-2020_10_26_351783	158	13	similar	similar	JJ
10_1101-2020_10_26_351783	158	14	benchmark	benchmark	JJ
10_1101-2020_10_26_351783	158	15	study	study	NN
10_1101-2020_10_26_351783	158	16	for	for	IN
10_1101-2020_10_26_351783	158	17	methylation	methylation	NN
10_1101-2020_10_26_351783	158	18	modules	module	NNS
10_1101-2020_10_26_351783	158	19	.	.	.
10_1101-2020_10_26_351783	159	1	We	-PRON-	PRP
10_1101-2020_10_26_351783	159	2	collected	collect	VBD
10_1101-2020_10_26_351783	159	3	ten	ten	JJ
10_1101-2020_10_26_351783	159	4	datasets	dataset	NNS
10_1101-2020_10_26_351783	159	5	from	from	IN
10_1101-2020_10_26_351783	159	6	three	three	CD
10_1101-2020_10_26_351783	159	7	different	different	JJ
10_1101-2020_10_26_351783	159	8	disease	disease	NN
10_1101-2020_10_26_351783	159	9	categories	category	NNS
10_1101-2020_10_26_351783	159	10	,	,	,
10_1101-2020_10_26_351783	159	11	including	include	VBG
10_1101-2020_10_26_351783	159	12	six	six	CD
10_1101-2020_10_26_351783	159	13	complex	complex	JJ
10_1101-2020_10_26_351783	159	14	diseases	disease	NNS
10_1101-2020_10_26_351783	159	15	,	,	,
10_1101-2020_10_26_351783	159	16	and	and	CC
10_1101-2020_10_26_351783	159	17	ran	run	VBD
10_1101-2020_10_26_351783	159	18	the	the	DT
10_1101-2020_10_26_351783	159	19	eight	eight	CD
10_1101-2020_10_26_351783	159	20	MODifieR	MODifieR	NNP
10_1101-2020_10_26_351783	159	21	methods	method	NNS
10_1101-2020_10_26_351783	159	22	on	on	IN
10_1101-2020_10_26_351783	159	23	them	-PRON-	PRP
10_1101-2020_10_26_351783	159	24	(	(	-LRB-
10_1101-2020_10_26_351783	159	25	Fig	fig	NN
10_1101-2020_10_26_351783	159	26	.	.	.
10_1101-2020_10_26_351783	160	1	1a	1a	LS
10_1101-2020_10_26_351783	160	2	)	)	-RRB-
10_1101-2020_10_26_351783	160	3	.	.	.
10_1101-2020_10_26_351783	161	1	In	in	IN
10_1101-2020_10_26_351783	161	2	addition	addition	NN
10_1101-2020_10_26_351783	161	3	,	,	,
10_1101-2020_10_26_351783	161	4	we	-PRON-	PRP
10_1101-2020_10_26_351783	161	5	constructed	construct	VBD
10_1101-2020_10_26_351783	161	6	consensus	consensus	NN
10_1101-2020_10_26_351783	161	7	modules	module	NNS
10_1101-2020_10_26_351783	161	8	for	for	IN
10_1101-2020_10_26_351783	161	9	each	each	DT
10_1101-2020_10_26_351783	161	10	of	of	IN
10_1101-2020_10_26_351783	161	11	the	the	DT
10_1101-2020_10_26_351783	161	12	datasets	dataset	NNS
10_1101-2020_10_26_351783	161	13	.	.	.
10_1101-2020_10_26_351783	162	1	Modules	module	NNS
10_1101-2020_10_26_351783	162	2	were	be	VBD
10_1101-2020_10_26_351783	162	3	then	then	RB
10_1101-2020_10_26_351783	162	4	tested	test	VBN
10_1101-2020_10_26_351783	162	5	for	for	IN
10_1101-2020_10_26_351783	162	6	GWAS	GWAS	NNP
10_1101-2020_10_26_351783	162	7	enrichment	enrichment	NN
10_1101-2020_10_26_351783	162	8	using	use	VBG
10_1101-2020_10_26_351783	162	9	Pascal	Pascal	NNP
10_1101-2020_10_26_351783	162	10	.	.	.
10_1101-2020_10_26_351783	163	1	Inspecting	inspect	VBG
10_1101-2020_10_26_351783	163	2	the	the	DT
10_1101-2020_10_26_351783	163	3	overall	overall	JJ
10_1101-2020_10_26_351783	163	4	performance	performance	NN
10_1101-2020_10_26_351783	163	5	,	,	,
10_1101-2020_10_26_351783	163	6	we	-PRON-	PRP
10_1101-2020_10_26_351783	163	7	found	find	VBD
10_1101-2020_10_26_351783	163	8	nine	nine	CD
10_1101-2020_10_26_351783	163	9	single	single	JJ
10_1101-2020_10_26_351783	163	10	-	-	HYPH
10_1101-2020_10_26_351783	163	11	method	method	NN
10_1101-2020_10_26_351783	163	12	modules	module	NNS
10_1101-2020_10_26_351783	163	13	with	with	IN
10_1101-2020_10_26_351783	163	14	a	a	DT
10_1101-2020_10_26_351783	163	15	significant	significant	JJ
10_1101-2020_10_26_351783	163	16	GWAS	GWAS	NNP
10_1101-2020_10_26_351783	163	17	enrichment	enrichment	NN
10_1101-2020_10_26_351783	163	18	(	(	-LRB-
10_1101-2020_10_26_351783	163	19	9/72	9/72	CD
10_1101-2020_10_26_351783	163	20	,	,	,
10_1101-2020_10_26_351783	163	21	11.8	11.8	CD
10_1101-2020_10_26_351783	163	22	%	%	NN
10_1101-2020_10_26_351783	163	23	)	)	-RRB-
10_1101-2020_10_26_351783	163	24	.	.	.
10_1101-2020_10_26_351783	164	1	Though	though	IN
10_1101-2020_10_26_351783	164	2	this	this	DT
10_1101-2020_10_26_351783	164	3	might	may	MD
10_1101-2020_10_26_351783	164	4	be	be	VB
10_1101-2020_10_26_351783	164	5	due	due	IN
10_1101-2020_10_26_351783	164	6	to	to	IN
10_1101-2020_10_26_351783	164	7	disease	disease	NN
10_1101-2020_10_26_351783	164	8	and	and	CC
10_1101-2020_10_26_351783	164	9	cell	cell	NN
10_1101-2020_10_26_351783	164	10	type	type	NN
10_1101-2020_10_26_351783	164	11	heterogeneity	heterogeneity	NN
10_1101-2020_10_26_351783	164	12	,	,	,
10_1101-2020_10_26_351783	164	13	the	the	DT
10_1101-2020_10_26_351783	164	14	enrichment	enrichment	NN
10_1101-2020_10_26_351783	164	15	is	be	VBZ
10_1101-2020_10_26_351783	164	16	more	more	JJR
10_1101-2020_10_26_351783	164	17	than	than	IN
10_1101-2020_10_26_351783	164	18	expected	expect	VBN
10_1101-2020_10_26_351783	164	19	by	by	IN
10_1101-2020_10_26_351783	164	20	chance	chance	NN
10_1101-2020_10_26_351783	164	21	(	(	-LRB-
10_1101-2020_10_26_351783	164	22	P=9.6x	P=9.6x	NNP
10_1101-2020_10_26_351783	164	23	10	10	CD
10_1101-2020_10_26_351783	164	24	-	-	SYM
10_1101-2020_10_26_351783	164	25	3	3	CD
10_1101-2020_10_26_351783	164	26	)	)	-RRB-
10_1101-2020_10_26_351783	164	27	.	.	.
10_1101-2020_10_26_351783	165	1	Interestingly	interestingly	RB
10_1101-2020_10_26_351783	165	2	,	,	,
10_1101-2020_10_26_351783	165	3	the	the	DT
10_1101-2020_10_26_351783	165	4	inflammatory	inflammatory	JJ
10_1101-2020_10_26_351783	165	5	diseases	disease	NNS
10_1101-2020_10_26_351783	165	6	such	such	JJ
10_1101-2020_10_26_351783	165	7	as	as	IN
10_1101-2020_10_26_351783	165	8	MS	MS	NNP
10_1101-2020_10_26_351783	165	9	and	and	CC
10_1101-2020_10_26_351783	165	10	UC	UC	NNP
10_1101-2020_10_26_351783	165	11	showed	show	VBD
10_1101-2020_10_26_351783	165	12	a	a	DT
10_1101-2020_10_26_351783	165	13	more	more	RBR
10_1101-2020_10_26_351783	165	14	significant	significant	JJ
10_1101-2020_10_26_351783	165	15	GWAS	GWAS	NNP
10_1101-2020_10_26_351783	165	16	enrichment	enrichment	NN
10_1101-2020_10_26_351783	165	17	Considering	consider	VBG
10_1101-2020_10_26_351783	165	18	that	that	IN
10_1101-2020_10_26_351783	165	19	the	the	DT
10_1101-2020_10_26_351783	165	20	evaluation	evaluation	NN
10_1101-2020_10_26_351783	165	21	of	of	IN
10_1101-2020_10_26_351783	165	22	module	module	JJ
10_1101-2020_10_26_351783	165	23	performance	performance	NN
10_1101-2020_10_26_351783	165	24	by	by	IN
10_1101-2020_10_26_351783	165	25	GWAS	GWAS	NNP
10_1101-2020_10_26_351783	165	26	enrichment	enrichment	NN
10_1101-2020_10_26_351783	165	27	may	may	MD
10_1101-2020_10_26_351783	165	28	be	be	VB
10_1101-2020_10_26_351783	165	29	biased	biased	JJ
10_1101-2020_10_26_351783	165	30	due	due	IN
10_1101-2020_10_26_351783	165	31	to	to	IN
10_1101-2020_10_26_351783	165	32	differences	difference	NNS
10_1101-2020_10_26_351783	165	33	in	in	IN
10_1101-2020_10_26_351783	165	34	module	module	JJ
10_1101-2020_10_26_351783	165	35	sizes	size	NNS
10_1101-2020_10_26_351783	165	36	and	and	CC
10_1101-2020_10_26_351783	165	37	interactome	interactome	JJ
10_1101-2020_10_26_351783	165	38	centrality	centrality	NN
10_1101-2020_10_26_351783	165	39	,	,	,
10_1101-2020_10_26_351783	165	40	we	-PRON-	PRP
10_1101-2020_10_26_351783	165	41	again	again	RB
10_1101-2020_10_26_351783	165	42	assessed	assess	VBD
10_1101-2020_10_26_351783	165	43	the	the	DT
10_1101-2020_10_26_351783	165	44	correlation	correlation	NN
10_1101-2020_10_26_351783	165	45	between	between	IN
10_1101-2020_10_26_351783	165	46	these	these	DT
10_1101-2020_10_26_351783	165	47	values	value	NNS
10_1101-2020_10_26_351783	165	48	.	.	.
10_1101-2020_10_26_351783	166	1	We	-PRON-	PRP
10_1101-2020_10_26_351783	166	2	found	find	VBD
10_1101-2020_10_26_351783	166	3	a	a	DT
10_1101-2020_10_26_351783	166	4	significant	significant	JJ
10_1101-2020_10_26_351783	166	5	correlation	correlation	NN
10_1101-2020_10_26_351783	166	6	between	between	IN
10_1101-2020_10_26_351783	166	7	GWAS	GWAS	NNP
10_1101-2020_10_26_351783	166	8	enrichment	enrichment	NN
10_1101-2020_10_26_351783	166	9	and	and	CC
10_1101-2020_10_26_351783	166	10	module	module	JJ
10_1101-2020_10_26_351783	166	11	size	size	NN
10_1101-2020_10_26_351783	166	12	(	(	-LRB-
10_1101-2020_10_26_351783	166	13	Fig	fig	NN
10_1101-2020_10_26_351783	166	14	.	.	.
10_1101-2020_10_26_351783	167	1	3c	3c	LS
10_1101-2020_10_26_351783	167	2	,	,	,
10_1101-2020_10_26_351783	167	3	rho	rho	NN
10_1101-2020_10_26_351783	167	4	=	=	SYM
10_1101-2020_10_26_351783	167	5	0.235	0.235	CD
10_1101-2020_10_26_351783	167	6	,	,	,
10_1101-2020_10_26_351783	167	7	P	p	NN
10_1101-2020_10_26_351783	167	8	=	=	SYM
10_1101-2020_10_26_351783	167	9	0.046	0.046	CD
10_1101-2020_10_26_351783	167	10	)	)	-RRB-
10_1101-2020_10_26_351783	167	11	and	and	CC
10_1101-2020_10_26_351783	167	12	a	a	DT
10_1101-2020_10_26_351783	167	13	non	non	JJ
10_1101-2020_10_26_351783	167	14	-	-	JJ
10_1101-2020_10_26_351783	167	15	significant	significant	JJ
10_1101-2020_10_26_351783	167	16	correlation	correlation	NN
10_1101-2020_10_26_351783	167	17	between	between	IN
10_1101-2020_10_26_351783	167	18	GWAS	GWAS	NNP
10_1101-2020_10_26_351783	167	19	enrichment	enrichment	NN
10_1101-2020_10_26_351783	167	20	and	and	CC
10_1101-2020_10_26_351783	167	21	interactome	interactome	JJ
10_1101-2020_10_26_351783	167	22	centrality	centrality	NN
10_1101-2020_10_26_351783	167	23	(	(	-LRB-
10_1101-2020_10_26_351783	167	24	Fig	fig	NN
10_1101-2020_10_26_351783	167	25	.	.	.
10_1101-2020_10_26_351783	168	1	3b	3b	LS
10_1101-2020_10_26_351783	168	2	,	,	,
10_1101-2020_10_26_351783	168	3	rho	rho	NNP
10_1101-2020_10_26_351783	168	4	=	=	SYM
10_1101-2020_10_26_351783	168	5	0.190	0.190	CD
10_1101-2020_10_26_351783	168	6	,	,	,
10_1101-2020_10_26_351783	168	7	P	p	NN
10_1101-2020_10_26_351783	168	8	=	=	SYM
10_1101-2020_10_26_351783	168	9	0.109	0.109	CD
10_1101-2020_10_26_351783	168	10	)	)	-RRB-
10_1101-2020_10_26_351783	168	11	.	.	.
10_1101-2020_10_26_351783	169	1	We	-PRON-	PRP
10_1101-2020_10_26_351783	169	2	found	find	VBD
10_1101-2020_10_26_351783	169	3	that	that	IN
10_1101-2020_10_26_351783	169	4	12.5	12.5	CD
10_1101-2020_10_26_351783	169	5	%	%	NN
10_1101-2020_10_26_351783	169	6	of	of	IN
10_1101-2020_10_26_351783	169	7	the	the	DT
10_1101-2020_10_26_351783	169	8	disease	disease	NN
10_1101-2020_10_26_351783	169	9	-	-	HYPH
10_1101-2020_10_26_351783	169	10	method	method	NN
10_1101-2020_10_26_351783	169	11	combinations	combination	NNS
10_1101-2020_10_26_351783	169	12	yielded	yield	VBD
10_1101-2020_10_26_351783	169	13	significant	significant	JJ
10_1101-2020_10_26_351783	169	14	GWAS	GWAS	NNP
10_1101-2020_10_26_351783	169	15	enrichment	enrichment	NN
10_1101-2020_10_26_351783	169	16	,	,	,
10_1101-2020_10_26_351783	169	17	which	which	WDT
10_1101-2020_10_26_351783	169	18	is	be	VBZ
10_1101-2020_10_26_351783	169	19	more	more	JJR
10_1101-2020_10_26_351783	169	20	than	than	IN
10_1101-2020_10_26_351783	169	21	expected	expect	VBN
10_1101-2020_10_26_351783	169	22	from	from	IN
10_1101-2020_10_26_351783	169	23	an	an	DT
10_1101-2020_10_26_351783	169	24	independent	independent	JJ
10_1101-2020_10_26_351783	169	25	random	random	JJ
10_1101-2020_10_26_351783	169	26	selection	selection	NN
10_1101-2020_10_26_351783	169	27	of	of	IN
10_1101-2020_10_26_351783	169	28	modules	module	NNS
10_1101-2020_10_26_351783	169	29	(	(	-LRB-
10_1101-2020_10_26_351783	169	30	Fisher	Fisher	NNP
10_1101-2020_10_26_351783	169	31	’s	’s	POS
10_1101-2020_10_26_351783	169	32	exact	exact	JJ
10_1101-2020_10_26_351783	169	33	test	test	NN
10_1101-2020_10_26_351783	169	34	P	p	NN
10_1101-2020_10_26_351783	169	35	=	=	SYM
10_1101-2020_10_26_351783	169	36	0.031	0.031	CD
10_1101-2020_10_26_351783	169	37	,	,	,
10_1101-2020_10_26_351783	169	38	n	n	CC
10_1101-2020_10_26_351783	169	39	=	=	SYM
10_1101-2020_10_26_351783	169	40	6	6	CD
10_1101-2020_10_26_351783	169	41	)	)	-RRB-
10_1101-2020_10_26_351783	169	42	.	.	.
10_1101-2020_10_26_351783	170	1	The	the	DT
10_1101-2020_10_26_351783	170	2	highly	highly	RB
10_1101-2020_10_26_351783	170	3	enriched	enriched	JJ
10_1101-2020_10_26_351783	170	4	disease	disease	NN
10_1101-2020_10_26_351783	170	5	modules	module	NNS
10_1101-2020_10_26_351783	170	6	belong	belong	VBP
10_1101-2020_10_26_351783	170	7	to	to	IN
10_1101-2020_10_26_351783	170	8	MS	MS	NNP
10_1101-2020_10_26_351783	170	9	,	,	,
10_1101-2020_10_26_351783	170	10	UC	UC	NNP
10_1101-2020_10_26_351783	170	11	and	and	CC
10_1101-2020_10_26_351783	170	12	CD	CD	NNP
10_1101-2020_10_26_351783	170	13	.	.	.
10_1101-2020_10_26_351783	171	1	Two	two	CD
10_1101-2020_10_26_351783	171	2	out	out	IN
10_1101-2020_10_26_351783	171	3	of	of	IN
10_1101-2020_10_26_351783	171	4	the	the	DT
10_1101-2020_10_26_351783	171	5	six	six	CD
10_1101-2020_10_26_351783	171	6	diseases	disease	NNS
10_1101-2020_10_26_351783	171	7	showed	show	VBD
10_1101-2020_10_26_351783	171	8	significant	significant	JJ
10_1101-2020_10_26_351783	171	9	GWAS	GWAS	NNP
10_1101-2020_10_26_351783	171	10	(	(	-LRB-
10_1101-2020_10_26_351783	171	11	which	which	WDT
10_1101-2020_10_26_351783	171	12	was	be	VBD
10_1101-2020_10_26_351783	171	13	not	not	RB
10_1101-2020_10_26_351783	171	14	certified	certify	VBN
10_1101-2020_10_26_351783	171	15	by	by	IN
10_1101-2020_10_26_351783	171	16	peer	peer	NN
10_1101-2020_10_26_351783	171	17	review	review	NN
10_1101-2020_10_26_351783	171	18	)	)	-RRB-
10_1101-2020_10_26_351783	171	19	is	be	VBZ
10_1101-2020_10_26_351783	171	20	the	the	DT
10_1101-2020_10_26_351783	171	21	author	author	NN
10_1101-2020_10_26_351783	171	22	/	/	SYM
10_1101-2020_10_26_351783	171	23	funder	funder	NN
10_1101-2020_10_26_351783	171	24	.	.	.
10_1101-2020_10_26_351783	172	1	All	all	DT
10_1101-2020_10_26_351783	172	2	rights	right	NNS
10_1101-2020_10_26_351783	172	3	reserved	reserve	VBD
10_1101-2020_10_26_351783	172	4	.	.	.
10_1101-2020_10_26_351783	173	1	No	no	DT
10_1101-2020_10_26_351783	173	2	reuse	reuse	NN
10_1101-2020_10_26_351783	173	3	allowed	allow	VBN
10_1101-2020_10_26_351783	173	4	without	without	IN
10_1101-2020_10_26_351783	173	5	permission	permission	NN
10_1101-2020_10_26_351783	173	6	.	.	.
10_1101-2020_10_26_351783	174	1	The	the	DT
10_1101-2020_10_26_351783	174	2	copyright	copyright	NN
10_1101-2020_10_26_351783	174	3	holder	holder	NN
10_1101-2020_10_26_351783	174	4	for	for	IN
10_1101-2020_10_26_351783	174	5	this	this	DT
10_1101-2020_10_26_351783	174	6	preprintthis	preprintthis	NN
10_1101-2020_10_26_351783	174	7	version	version	NN
10_1101-2020_10_26_351783	174	8	posted	post	VBD
10_1101-2020_10_26_351783	174	9	January	January	NNP
10_1101-2020_10_26_351783	174	10	6	6	CD
10_1101-2020_10_26_351783	174	11	,	,	,
10_1101-2020_10_26_351783	174	12	2021	2021	CD
10_1101-2020_10_26_351783	174	13	.	.	.
10_1101-2020_10_26_351783	174	14	;	;	:
10_1101-2020_10_26_351783	174	15	https://doi.org/10.1101/2020.10.26.351783doi	https://doi.org/10.1101/2020.10.26.351783doi	ADD
10_1101-2020_10_26_351783	174	16	:	:	:
10_1101-2020_10_26_351783	174	17	bioRxiv	biorxiv	IN
10_1101-2020_10_26_351783	174	18	preprint	preprint	NN
10_1101-2020_10_26_351783	174	19	https://doi.org/10.1101/2020.10.26.351783	https://doi.org/10.1101/2020.10.26.351783	CC
10_1101-2020_10_26_351783	174	20	12	12	CD
10_1101-2020_10_26_351783	174	21	enrichment	enrichment	NN
10_1101-2020_10_26_351783	174	22	by	by	IN
10_1101-2020_10_26_351783	174	23	using	use	VBG
10_1101-2020_10_26_351783	174	24	the	the	DT
10_1101-2020_10_26_351783	174	25	Clique	Clique	NNP
10_1101-2020_10_26_351783	174	26	SuM	sum	NN
10_1101-2020_10_26_351783	174	27	modules	module	NNS
10_1101-2020_10_26_351783	174	28	(	(	-LRB-
10_1101-2020_10_26_351783	174	29	P	p	NN
10_1101-2020_10_26_351783	174	30	=	=	SYM
10_1101-2020_10_26_351783	174	31	0.032	0.032	CD
10_1101-2020_10_26_351783	174	32	)	)	-RRB-
10_1101-2020_10_26_351783	174	33	.	.	.
10_1101-2020_10_26_351783	175	1	In	in	IN
10_1101-2020_10_26_351783	175	2	summary	summary	NN
10_1101-2020_10_26_351783	175	3	,	,	,
10_1101-2020_10_26_351783	175	4	Clique	Clique	NNP
10_1101-2020_10_26_351783	175	5	SuM	sum	NN
10_1101-2020_10_26_351783	175	6	method	method	NN
10_1101-2020_10_26_351783	175	7	resulted	result	VBD
10_1101-2020_10_26_351783	175	8	in	in	IN
10_1101-2020_10_26_351783	175	9	a	a	DT
10_1101-2020_10_26_351783	175	10	more	more	RBR
10_1101-2020_10_26_351783	175	11	significant	significant	JJ
10_1101-2020_10_26_351783	175	12	GWAS	GWAS	NNP
10_1101-2020_10_26_351783	175	13	enrichment	enrichment	NN
10_1101-2020_10_26_351783	175	14	for	for	IN
10_1101-2020_10_26_351783	175	15	most	most	JJS
10_1101-2020_10_26_351783	175	16	diseases	disease	NNS
10_1101-2020_10_26_351783	175	17	also	also	RB
10_1101-2020_10_26_351783	175	18	for	for	IN
10_1101-2020_10_26_351783	175	19	the	the	DT
10_1101-2020_10_26_351783	175	20	methylomic	methylomic	JJ
10_1101-2020_10_26_351783	175	21	benchmark	benchmark	NN
10_1101-2020_10_26_351783	175	22	.	.	.
10_1101-2020_10_26_351783	176	1	Multi	multi	JJ
10_1101-2020_10_26_351783	176	2	-	-	HYPH
10_1101-2020_10_26_351783	176	3	omics	omics	NNP
10_1101-2020_10_26_351783	176	4	approach	approach	NN
10_1101-2020_10_26_351783	176	5	revealed	reveal	VBD
10_1101-2020_10_26_351783	176	6	a	a	DT
10_1101-2020_10_26_351783	176	7	module	module	NN
10_1101-2020_10_26_351783	176	8	enriched	enrich	VBN
10_1101-2020_10_26_351783	176	9	for	for	IN
10_1101-2020_10_26_351783	176	10	MS	MS	NNP
10_1101-2020_10_26_351783	176	11	-	-	HYPH
10_1101-2020_10_26_351783	176	12	associated	associate	VBN
10_1101-2020_10_26_351783	176	13	genes	gene	NNS
10_1101-2020_10_26_351783	176	14	.	.	.
10_1101-2020_10_26_351783	177	1	Considering	consider	VBG
10_1101-2020_10_26_351783	177	2	genomic	genomic	JJ
10_1101-2020_10_26_351783	177	3	concordance	concordance	NN
10_1101-2020_10_26_351783	177	4	as	as	IN
10_1101-2020_10_26_351783	177	5	the	the	DT
10_1101-2020_10_26_351783	177	6	guidance	guidance	NN
10_1101-2020_10_26_351783	177	7	principle	principle	NN
10_1101-2020_10_26_351783	177	8	for	for	IN
10_1101-2020_10_26_351783	177	9	the	the	DT
10_1101-2020_10_26_351783	177	10	modules	module	NNS
10_1101-2020_10_26_351783	177	11	that	that	WDT
10_1101-2020_10_26_351783	177	12	show	show	VBP
10_1101-2020_10_26_351783	177	13	enrichment	enrichment	NN
10_1101-2020_10_26_351783	177	14	for	for	IN
10_1101-2020_10_26_351783	177	15	GWAS	GWAS	NNP
10_1101-2020_10_26_351783	177	16	SNPs	snp	NNS
10_1101-2020_10_26_351783	177	17	,	,	,
10_1101-2020_10_26_351783	177	18	differentially	differentially	RB
10_1101-2020_10_26_351783	177	19	methylated	methylate	VBN
10_1101-2020_10_26_351783	177	20	genes	gene	NNS
10_1101-2020_10_26_351783	177	21	and	and	CC
10_1101-2020_10_26_351783	177	22	differentially	differentially	RB
10_1101-2020_10_26_351783	177	23	expressed	express	VBD
10_1101-2020_10_26_351783	177	24	genes	gene	NNS
10_1101-2020_10_26_351783	177	25	,	,	,
10_1101-2020_10_26_351783	177	26	we	-PRON-	PRP
10_1101-2020_10_26_351783	177	27	further	further	RB
10_1101-2020_10_26_351783	177	28	wanted	want	VBD
10_1101-2020_10_26_351783	177	29	to	to	TO
10_1101-2020_10_26_351783	177	30	evaluate	evaluate	VB
10_1101-2020_10_26_351783	177	31	multiple	multiple	JJ
10_1101-2020_10_26_351783	177	32	datasets	dataset	NNS
10_1101-2020_10_26_351783	177	33	of	of	IN
10_1101-2020_10_26_351783	177	34	one	one	CD
10_1101-2020_10_26_351783	177	35	specific	specific	JJ
10_1101-2020_10_26_351783	177	36	disease	disease	NN
10_1101-2020_10_26_351783	177	37	,	,	,
10_1101-2020_10_26_351783	177	38	i.e.	i.e.	FW
10_1101-2020_10_26_351783	177	39	,	,	,
10_1101-2020_10_26_351783	177	40	MS	MS	NNP
10_1101-2020_10_26_351783	177	41	.	.	.
10_1101-2020_10_26_351783	177	42	We	-PRON-	PRP
10_1101-2020_10_26_351783	177	43	compiled	compile	VBD
10_1101-2020_10_26_351783	177	44	11	11	CD
10_1101-2020_10_26_351783	177	45	MS	MS	NNP
10_1101-2020_10_26_351783	177	46	transcriptomic	transcriptomic	JJ
10_1101-2020_10_26_351783	177	47	datasets	dataset	NNS
10_1101-2020_10_26_351783	177	48	and	and	CC
10_1101-2020_10_26_351783	177	49	nine	nine	CD
10_1101-2020_10_26_351783	177	50	methylation	methylation	NN
10_1101-2020_10_26_351783	177	51	(	(	-LRB-
10_1101-2020_10_26_351783	177	52	Supplementary	Supplementary	NNP
10_1101-2020_10_26_351783	177	53	Table	Table	NNP
10_1101-2020_10_26_351783	177	54	2	2	CD
10_1101-2020_10_26_351783	177	55	)	)	-RRB-
10_1101-2020_10_26_351783	177	56	comparisons	comparison	NNS
10_1101-2020_10_26_351783	177	57	from	from	IN
10_1101-2020_10_26_351783	177	58	GEO	GEO	NNP
10_1101-2020_10_26_351783	177	59	which	which	WDT
10_1101-2020_10_26_351783	177	60	satisfy	satisfy	VBP
10_1101-2020_10_26_351783	177	61	the	the	DT
10_1101-2020_10_26_351783	177	62	pre	pre	VBN
10_1101-2020_10_26_351783	177	63	-	-	JJ
10_1101-2020_10_26_351783	177	64	defined	define	VBN
10_1101-2020_10_26_351783	177	65	dataset	dataset	NN
10_1101-2020_10_26_351783	177	66	criteria	criterion	NNS
10_1101-2020_10_26_351783	177	67	(	(	-LRB-
10_1101-2020_10_26_351783	177	68	see	see	VB
10_1101-2020_10_26_351783	177	69	Methods	Methods	NNP
10_1101-2020_10_26_351783	177	70	)	)	-RRB-
10_1101-2020_10_26_351783	177	71	.	.	.
10_1101-2020_10_26_351783	178	1	For	for	IN
10_1101-2020_10_26_351783	178	2	each	each	DT
10_1101-2020_10_26_351783	178	3	dataset	dataset	NN
10_1101-2020_10_26_351783	178	4	we	-PRON-	PRP
10_1101-2020_10_26_351783	178	5	implemented	implement	VBD
10_1101-2020_10_26_351783	178	6	the	the	DT
10_1101-2020_10_26_351783	178	7	pipeline	pipeline	NN
10_1101-2020_10_26_351783	178	8	for	for	IN
10_1101-2020_10_26_351783	178	9	module	module	JJ
10_1101-2020_10_26_351783	178	10	identification	identification	NN
10_1101-2020_10_26_351783	178	11	and	and	CC
10_1101-2020_10_26_351783	178	12	scoring	score	VBG
10_1101-2020_10_26_351783	178	13	shown	show	VBN
10_1101-2020_10_26_351783	178	14	in	in	IN
10_1101-2020_10_26_351783	178	15	Fig	Fig	NNP
10_1101-2020_10_26_351783	178	16	.	.	.
10_1101-2020_10_26_351783	179	1	1b	1b	CD
10_1101-2020_10_26_351783	179	2	.	.	.
10_1101-2020_10_26_351783	180	1	We	-PRON-	PRP
10_1101-2020_10_26_351783	180	2	evaluated	evaluate	VBD
10_1101-2020_10_26_351783	180	3	each	each	DT
10_1101-2020_10_26_351783	180	4	module	module	NN
10_1101-2020_10_26_351783	180	5	using	use	VBG
10_1101-2020_10_26_351783	180	6	MS	MS	NNP
10_1101-2020_10_26_351783	180	7	SNP	SNP	NNP
10_1101-2020_10_26_351783	180	8	enrichment	enrichment	NN
10_1101-2020_10_26_351783	180	9	analysis	analysis	NN
10_1101-2020_10_26_351783	180	10	and	and	CC
10_1101-2020_10_26_351783	180	11	selected	select	VBD
10_1101-2020_10_26_351783	180	12	the	the	DT
10_1101-2020_10_26_351783	180	13	most	most	RBS
10_1101-2020_10_26_351783	180	14	enriched	enriched	JJ
10_1101-2020_10_26_351783	180	15	modules	module	NNS
10_1101-2020_10_26_351783	180	16	per	per	IN
10_1101-2020_10_26_351783	180	17	omic	omic	JJ
10_1101-2020_10_26_351783	180	18	from	from	IN
10_1101-2020_10_26_351783	180	19	this	this	DT
10_1101-2020_10_26_351783	180	20	metric	metric	NN
10_1101-2020_10_26_351783	180	21	.	.	.
10_1101-2020_10_26_351783	181	1	This	this	DT
10_1101-2020_10_26_351783	181	2	analysis	analysis	NN
10_1101-2020_10_26_351783	181	3	again	again	RB
10_1101-2020_10_26_351783	181	4	showed	show	VBD
10_1101-2020_10_26_351783	181	5	that	that	IN
10_1101-2020_10_26_351783	181	6	Clique	Clique	NNP
10_1101-2020_10_26_351783	181	7	SuM	sum	NN
10_1101-2020_10_26_351783	181	8	yielded	yield	VBD
10_1101-2020_10_26_351783	181	9	the	the	DT
10_1101-2020_10_26_351783	181	10	far	far	RB
10_1101-2020_10_26_351783	181	11	highest	high	JJS
10_1101-2020_10_26_351783	181	12	average	average	JJ
10_1101-2020_10_26_351783	181	13	enrichment	enrichment	NN
10_1101-2020_10_26_351783	181	14	score	score	NN
10_1101-2020_10_26_351783	181	15	(	(	-LRB-
10_1101-2020_10_26_351783	181	16	meta	meta	NNP
10_1101-2020_10_26_351783	181	17	P	P	NNP
10_1101-2020_10_26_351783	181	18	=	=	SYM
10_1101-2020_10_26_351783	181	19	3.2	3.2	CD
10_1101-2020_10_26_351783	181	20	x	x	SYM
10_1101-2020_10_26_351783	181	21	10	10	CD
10_1101-2020_10_26_351783	181	22	-	-	SYM
10_1101-2020_10_26_351783	181	23	12	12	CD
10_1101-2020_10_26_351783	181	24	)	)	-RRB-
10_1101-2020_10_26_351783	181	25	and	and	CC
10_1101-2020_10_26_351783	181	26	was	be	VBD
10_1101-2020_10_26_351783	181	27	significantly	significantly	RB
10_1101-2020_10_26_351783	181	28	enriched	enrich	VBN
10_1101-2020_10_26_351783	181	29	(	(	-LRB-
10_1101-2020_10_26_351783	181	30	P	p	NN
10_1101-2020_10_26_351783	181	31	<	<	XX
10_1101-2020_10_26_351783	181	32	0.05	0.05	CD
10_1101-2020_10_26_351783	181	33	)	)	-RRB-
10_1101-2020_10_26_351783	181	34	in	in	IN
10_1101-2020_10_26_351783	181	35	9/11	9/11	CD
10_1101-2020_10_26_351783	181	36	transcriptomic	transcriptomic	JJ
10_1101-2020_10_26_351783	181	37	datasets	dataset	NNS
10_1101-2020_10_26_351783	181	38	(	(	-LRB-
10_1101-2020_10_26_351783	181	39	Fig	fig	NN
10_1101-2020_10_26_351783	181	40	.	.	.
10_1101-2020_10_26_351783	182	1	4a	4a	LS
10_1101-2020_10_26_351783	182	2	)	)	-RRB-
10_1101-2020_10_26_351783	182	3	and	and	CC
10_1101-2020_10_26_351783	182	4	4/9	4/9	CD
10_1101-2020_10_26_351783	182	5	of	of	IN
10_1101-2020_10_26_351783	182	6	the	the	DT
10_1101-2020_10_26_351783	182	7	methylation	methylation	NN
10_1101-2020_10_26_351783	182	8	datasets	dataset	NNS
10_1101-2020_10_26_351783	182	9	(	(	-LRB-
10_1101-2020_10_26_351783	182	10	Fig	fig	NN
10_1101-2020_10_26_351783	182	11	.	.	.
10_1101-2020_10_26_351783	183	1	4b	4b	LS
10_1101-2020_10_26_351783	183	2	)	)	-RRB-
10_1101-2020_10_26_351783	183	3	.	.	.
10_1101-2020_10_26_351783	184	1	From	from	IN
10_1101-2020_10_26_351783	184	2	the	the	DT
10_1101-2020_10_26_351783	184	3	significant	significant	JJ
10_1101-2020_10_26_351783	184	4	modules	module	NNS
10_1101-2020_10_26_351783	184	5	generated	generate	VBN
10_1101-2020_10_26_351783	184	6	by	by	IN
10_1101-2020_10_26_351783	184	7	Clique	Clique	NNP
10_1101-2020_10_26_351783	184	8	SuM	sum	NN
10_1101-2020_10_26_351783	184	9	,	,	,
10_1101-2020_10_26_351783	184	10	we	-PRON-	PRP
10_1101-2020_10_26_351783	184	11	choose	choose	VBP
10_1101-2020_10_26_351783	184	12	the	the	DT
10_1101-2020_10_26_351783	184	13	top	top	JJ
10_1101-2020_10_26_351783	184	14	four	four	CD
10_1101-2020_10_26_351783	184	15	modules	module	NNS
10_1101-2020_10_26_351783	184	16	from	from	IN
10_1101-2020_10_26_351783	184	17	each	each	DT
10_1101-2020_10_26_351783	184	18	of	of	IN
10_1101-2020_10_26_351783	184	19	the	the	DT
10_1101-2020_10_26_351783	184	20	gene	gene	NN
10_1101-2020_10_26_351783	184	21	transcription	transcription	NN
10_1101-2020_10_26_351783	184	22	and	and	CC
10_1101-2020_10_26_351783	184	23	methylation	methylation	NN
10_1101-2020_10_26_351783	184	24	sets	set	NNS
10_1101-2020_10_26_351783	184	25	,	,	,
10_1101-2020_10_26_351783	184	26	and	and	CC
10_1101-2020_10_26_351783	184	27	prioritized	prioritize	VBN
10_1101-2020_10_26_351783	184	28	genes	gene	NNS
10_1101-2020_10_26_351783	184	29	detected	detect	VBN
10_1101-2020_10_26_351783	184	30	in	in	IN
10_1101-2020_10_26_351783	184	31	modules	module	NNS
10_1101-2020_10_26_351783	184	32	from	from	IN
10_1101-2020_10_26_351783	184	33	multiple	multiple	JJ
10_1101-2020_10_26_351783	184	34	datasets	dataset	NNS
10_1101-2020_10_26_351783	184	35	in	in	IN
10_1101-2020_10_26_351783	184	36	each	each	DT
10_1101-2020_10_26_351783	184	37	omic	omic	JJ
10_1101-2020_10_26_351783	184	38	.	.	.
10_1101-2020_10_26_351783	185	1	This	this	DT
10_1101-2020_10_26_351783	185	2	analysis	analysis	NN
10_1101-2020_10_26_351783	185	3	showed	show	VBD
10_1101-2020_10_26_351783	185	4	that	that	IN
10_1101-2020_10_26_351783	185	5	the	the	DT
10_1101-2020_10_26_351783	185	6	strongest	strong	JJS
10_1101-2020_10_26_351783	185	7	MS	MS	NNP
10_1101-2020_10_26_351783	185	8	SNP	SNP	NNP
10_1101-2020_10_26_351783	185	9	enrichment	enrichment	NN
10_1101-2020_10_26_351783	185	10	was	be	VBD
10_1101-2020_10_26_351783	185	11	found	find	VBN
10_1101-2020_10_26_351783	185	12	for	for	IN
10_1101-2020_10_26_351783	185	13	genes	gene	NNS
10_1101-2020_10_26_351783	185	14	in	in	IN
10_1101-2020_10_26_351783	185	15	at	at	RB
10_1101-2020_10_26_351783	185	16	least	least	RBS
10_1101-2020_10_26_351783	185	17	three	three	CD
10_1101-2020_10_26_351783	185	18	out	out	IN
10_1101-2020_10_26_351783	185	19	of	of	IN
10_1101-2020_10_26_351783	185	20	four	four	CD
10_1101-2020_10_26_351783	185	21	transcriptomic	transcriptomic	JJ
10_1101-2020_10_26_351783	185	22	modules	module	NNS
10_1101-2020_10_26_351783	185	23	(	(	-LRB-
10_1101-2020_10_26_351783	185	24	n=1,552	n=1,552	NNP
10_1101-2020_10_26_351783	185	25	;	;	:
10_1101-2020_10_26_351783	185	26	P=	P=	NNP
10_1101-2020_10_26_351783	185	27	6.0	6.0	CD
10_1101-2020_10_26_351783	185	28	x	x	SYM
10_1101-2020_10_26_351783	185	29	10	10	CD
10_1101-2020_10_26_351783	185	30	-	-	SYM
10_1101-2020_10_26_351783	185	31	7	7	CD
10_1101-2020_10_26_351783	185	32	)	)	-RRB-
10_1101-2020_10_26_351783	185	33	and	and	CC
10_1101-2020_10_26_351783	185	34	two	two	CD
10_1101-2020_10_26_351783	185	35	out	out	IN
10_1101-2020_10_26_351783	185	36	of	of	IN
10_1101-2020_10_26_351783	185	37	four	four	CD
10_1101-2020_10_26_351783	185	38	methylomic	methylomic	JJ
10_1101-2020_10_26_351783	185	39	modules	module	NNS
10_1101-2020_10_26_351783	185	40	(	(	-LRB-
10_1101-2020_10_26_351783	185	41	n=324	n=324	NNP
10_1101-2020_10_26_351783	185	42	,	,	,
10_1101-2020_10_26_351783	185	43	P=	P=	NNP
10_1101-2020_10_26_351783	185	44	1.5x10	1.5x10	CD
10_1101-2020_10_26_351783	185	45	-	-	SYM
10_1101-2020_10_26_351783	185	46	6	6	CD
10_1101-2020_10_26_351783	185	47	)	)	-RRB-
10_1101-2020_10_26_351783	185	48	.	.	.
10_1101-2020_10_26_351783	186	1	Next	next	RB
10_1101-2020_10_26_351783	186	2	,	,	,
10_1101-2020_10_26_351783	186	3	we	-PRON-	PRP
10_1101-2020_10_26_351783	186	4	used	use	VBD
10_1101-2020_10_26_351783	186	5	the	the	DT
10_1101-2020_10_26_351783	186	6	same	same	JJ
10_1101-2020_10_26_351783	186	7	principle	principle	NN
10_1101-2020_10_26_351783	186	8	to	to	TO
10_1101-2020_10_26_351783	186	9	combine	combine	VB
10_1101-2020_10_26_351783	186	10	these	these	DT
10_1101-2020_10_26_351783	186	11	two	two	CD
10_1101-2020_10_26_351783	186	12	and	and	CC
10_1101-2020_10_26_351783	186	13	found	find	VBD
10_1101-2020_10_26_351783	186	14	that	that	IN
10_1101-2020_10_26_351783	186	15	the	the	DT
10_1101-2020_10_26_351783	186	16	intersection	intersection	NN
10_1101-2020_10_26_351783	186	17	between	between	IN
10_1101-2020_10_26_351783	186	18	the	the	DT
10_1101-2020_10_26_351783	186	19	gene	gene	NN
10_1101-2020_10_26_351783	186	20	transcription	transcription	NN
10_1101-2020_10_26_351783	186	21	and	and	CC
10_1101-2020_10_26_351783	186	22	methylation	methylation	NN
10_1101-2020_10_26_351783	186	23	consensus	consensus	NN
10_1101-2020_10_26_351783	186	24	resulted	result	VBD
10_1101-2020_10_26_351783	186	25	in	in	IN
10_1101-2020_10_26_351783	186	26	a	a	DT
10_1101-2020_10_26_351783	186	27	module	module	NN
10_1101-2020_10_26_351783	186	28	(	(	-LRB-
10_1101-2020_10_26_351783	186	29	n	n	NNP
10_1101-2020_10_26_351783	186	30	=	=	SYM
10_1101-2020_10_26_351783	186	31	220	220	CD
10_1101-2020_10_26_351783	186	32	genes	gene	NNS
10_1101-2020_10_26_351783	186	33	,	,	,
10_1101-2020_10_26_351783	186	34	Fig	Fig	NNP
10_1101-2020_10_26_351783	186	35	.	.	.
10_1101-2020_10_26_351783	187	1	4	4	LS
10_1101-2020_10_26_351783	187	2	)	)	-RRB-
10_1101-2020_10_26_351783	187	3	enriched	enrich	VBD
10_1101-2020_10_26_351783	187	4	for	for	IN
10_1101-2020_10_26_351783	187	5	MS	MS	NNP
10_1101-2020_10_26_351783	187	6	-	-	HYPH
10_1101-2020_10_26_351783	187	7	associated	associate	VBN
10_1101-2020_10_26_351783	187	8	genes	gene	NNS
10_1101-2020_10_26_351783	187	9	(	(	-LRB-
10_1101-2020_10_26_351783	187	10	75/220	75/220	CD
10_1101-2020_10_26_351783	187	11	,	,	,
10_1101-2020_10_26_351783	187	12	P	p	NN
10_1101-2020_10_26_351783	187	13	<	<	XX
10_1101-2020_10_26_351783	187	14	2.2	2.2	CD
10_1101-2020_10_26_351783	187	15	x	x	SYM
10_1101-2020_10_26_351783	187	16	10	10	CD
10_1101-2020_10_26_351783	187	17	-	-	SYM
10_1101-2020_10_26_351783	187	18	16	16	CD
10_1101-2020_10_26_351783	187	19	,	,	,
10_1101-2020_10_26_351783	187	20	OR	or	CC
10_1101-2020_10_26_351783	187	21	=	=	SYM
10_1101-2020_10_26_351783	187	22	7.8	7.8	CD
10_1101-2020_10_26_351783	187	23	)	)	-RRB-
10_1101-2020_10_26_351783	187	24	and	and	CC
10_1101-2020_10_26_351783	187	25	with	with	IN
10_1101-2020_10_26_351783	187	26	the	the	DT
10_1101-2020_10_26_351783	187	27	highest	high	JJS
10_1101-2020_10_26_351783	187	28	GWAS	gwas	JJ
10_1101-2020_10_26_351783	187	29	enrichment	enrichment	NN
10_1101-2020_10_26_351783	187	30	(	(	-LRB-
10_1101-2020_10_26_351783	187	31	P	p	NN
10_1101-2020_10_26_351783	187	32	=	=	SYM
10_1101-2020_10_26_351783	187	33	8.8	8.8	CD
10_1101-2020_10_26_351783	187	34	x	x	SYM
10_1101-2020_10_26_351783	187	35	10	10	CD
10_1101-2020_10_26_351783	187	36	-	-	SYM
10_1101-2020_10_26_351783	187	37	9	9	CD
10_1101-2020_10_26_351783	187	38	)	)	-RRB-
10_1101-2020_10_26_351783	187	39	which	which	WDT
10_1101-2020_10_26_351783	187	40	we	-PRON-	PRP
10_1101-2020_10_26_351783	187	41	hereafter	hereafter	RB
10_1101-2020_10_26_351783	187	42	referred	refer	VBD
10_1101-2020_10_26_351783	187	43	to	to	IN
10_1101-2020_10_26_351783	187	44	as	as	IN
10_1101-2020_10_26_351783	187	45	the	the	DT
10_1101-2020_10_26_351783	187	46	multi	multi	NNS
10_1101-2020_10_26_351783	187	47	-	-	HYPH
10_1101-2020_10_26_351783	187	48	omics	omic	NNS
10_1101-2020_10_26_351783	187	49	MS	MS	NNP
10_1101-2020_10_26_351783	187	50	module	module	NN
10_1101-2020_10_26_351783	187	51	.	.	.
10_1101-2020_10_26_351783	188	1	The	the	DT
10_1101-2020_10_26_351783	188	2	multi	multi	JJ
10_1101-2020_10_26_351783	188	3	-	-	HYPH
10_1101-2020_10_26_351783	188	4	omics	omic	NNS
10_1101-2020_10_26_351783	188	5	MS	MS	NNP
10_1101-2020_10_26_351783	188	6	module	module	NN
10_1101-2020_10_26_351783	188	7	was	be	VBD
10_1101-2020_10_26_351783	188	8	enriched	enrich	VBN
10_1101-2020_10_26_351783	188	9	in	in	IN
10_1101-2020_10_26_351783	188	10	genes	gene	NNS
10_1101-2020_10_26_351783	188	11	associated	associate	VBN
10_1101-2020_10_26_351783	188	12	with	with	IN
10_1101-2020_10_26_351783	188	13	major	major	JJ
10_1101-2020_10_26_351783	188	14	MS	MS	NNP
10_1101-2020_10_26_351783	188	15	pathways	pathway	NNS
10_1101-2020_10_26_351783	188	16	.	.	.
10_1101-2020_10_26_351783	189	1	As	as	IN
10_1101-2020_10_26_351783	189	2	we	-PRON-	PRP
10_1101-2020_10_26_351783	189	3	used	use	VBD
10_1101-2020_10_26_351783	189	4	GWAS	GWAS	NNP
10_1101-2020_10_26_351783	189	5	enrichment	enrichment	NN
10_1101-2020_10_26_351783	189	6	as	as	IN
10_1101-2020_10_26_351783	189	7	a	a	DT
10_1101-2020_10_26_351783	189	8	selection	selection	NN
10_1101-2020_10_26_351783	189	9	criterion	criterion	NN
10_1101-2020_10_26_351783	189	10	,	,	,
10_1101-2020_10_26_351783	189	11	the	the	DT
10_1101-2020_10_26_351783	189	12	high	high	JJ
10_1101-2020_10_26_351783	189	13	GWAS	GWAS	NNP
10_1101-2020_10_26_351783	189	14	enrichment	enrichment	NN
10_1101-2020_10_26_351783	189	15	of	of	IN
10_1101-2020_10_26_351783	189	16	the	the	DT
10_1101-2020_10_26_351783	189	17	final	final	JJ
10_1101-2020_10_26_351783	189	18	module	module	NN
10_1101-2020_10_26_351783	189	19	was	be	VBD
10_1101-2020_10_26_351783	189	20	partly	partly	RB
10_1101-2020_10_26_351783	189	21	expected	expect	VBN
10_1101-2020_10_26_351783	189	22	,	,	,
10_1101-2020_10_26_351783	189	23	which	which	WDT
10_1101-2020_10_26_351783	189	24	led	lead	VBD
10_1101-2020_10_26_351783	189	25	us	-PRON-	PRP
10_1101-2020_10_26_351783	189	26	to	to	TO
10_1101-2020_10_26_351783	189	27	analyze	analyze	VB
10_1101-2020_10_26_351783	189	28	its	-PRON-	PRP$
10_1101-2020_10_26_351783	189	29	biological	biological	JJ
10_1101-2020_10_26_351783	189	30	functions	function	NNS
10_1101-2020_10_26_351783	189	31	and	and	CC
10_1101-2020_10_26_351783	189	32	their	-PRON-	PRP$
10_1101-2020_10_26_351783	189	33	potential	potential	JJ
10_1101-2020_10_26_351783	189	34	epigenetic	epigenetic	JJ
10_1101-2020_10_26_351783	189	35	associations	association	NNS
10_1101-2020_10_26_351783	189	36	to	to	IN
10_1101-2020_10_26_351783	189	37	MS	MS	NNP
10_1101-2020_10_26_351783	189	38	.	.	.
10_1101-2020_10_26_351783	189	39	First	first	RB
10_1101-2020_10_26_351783	189	40	,	,	,
10_1101-2020_10_26_351783	189	41	pathway	pathway	JJ
10_1101-2020_10_26_351783	189	42	enrichment	enrichment	NN
10_1101-2020_10_26_351783	189	43	analysis	analysis	NN
10_1101-2020_10_26_351783	189	44	showed	show	VBD
10_1101-2020_10_26_351783	189	45	that	that	IN
10_1101-2020_10_26_351783	189	46	the	the	DT
10_1101-2020_10_26_351783	189	47	multi	multi	JJ
10_1101-2020_10_26_351783	189	48	-	-	JJ
10_1101-2020_10_26_351783	189	49	omics	omics	JJ
10_1101-2020_10_26_351783	189	50	module	module	NNP
10_1101-2020_10_26_351783	189	51	genes	gene	NNS
10_1101-2020_10_26_351783	189	52	(	(	-LRB-
10_1101-2020_10_26_351783	189	53	which	which	WDT
10_1101-2020_10_26_351783	189	54	was	be	VBD
10_1101-2020_10_26_351783	189	55	not	not	RB
10_1101-2020_10_26_351783	189	56	certified	certify	VBN
10_1101-2020_10_26_351783	189	57	by	by	IN
10_1101-2020_10_26_351783	189	58	peer	peer	NN
10_1101-2020_10_26_351783	189	59	review	review	NN
10_1101-2020_10_26_351783	189	60	)	)	-RRB-
10_1101-2020_10_26_351783	189	61	is	be	VBZ
10_1101-2020_10_26_351783	189	62	the	the	DT
10_1101-2020_10_26_351783	189	63	author	author	NN
10_1101-2020_10_26_351783	189	64	/	/	SYM
10_1101-2020_10_26_351783	189	65	funder	funder	NN
10_1101-2020_10_26_351783	189	66	.	.	.
10_1101-2020_10_26_351783	190	1	All	all	DT
10_1101-2020_10_26_351783	190	2	rights	right	NNS
10_1101-2020_10_26_351783	190	3	reserved	reserve	VBD
10_1101-2020_10_26_351783	190	4	.	.	.
10_1101-2020_10_26_351783	191	1	No	no	DT
10_1101-2020_10_26_351783	191	2	reuse	reuse	NN
10_1101-2020_10_26_351783	191	3	allowed	allow	VBN
10_1101-2020_10_26_351783	191	4	without	without	IN
10_1101-2020_10_26_351783	191	5	permission	permission	NN
10_1101-2020_10_26_351783	191	6	.	.	.
10_1101-2020_10_26_351783	192	1	The	the	DT
10_1101-2020_10_26_351783	192	2	copyright	copyright	NN
10_1101-2020_10_26_351783	192	3	holder	holder	NN
10_1101-2020_10_26_351783	192	4	for	for	IN
10_1101-2020_10_26_351783	192	5	this	this	DT
10_1101-2020_10_26_351783	192	6	preprintthis	preprintthis	NN
10_1101-2020_10_26_351783	192	7	version	version	NN
10_1101-2020_10_26_351783	192	8	posted	post	VBD
10_1101-2020_10_26_351783	192	9	January	January	NNP
10_1101-2020_10_26_351783	192	10	6	6	CD
10_1101-2020_10_26_351783	192	11	,	,	,
10_1101-2020_10_26_351783	192	12	2021	2021	CD
10_1101-2020_10_26_351783	192	13	.	.	.
10_1101-2020_10_26_351783	192	14	;	;	:
10_1101-2020_10_26_351783	192	15	https://doi.org/10.1101/2020.10.26.351783doi	https://doi.org/10.1101/2020.10.26.351783doi	ADD
10_1101-2020_10_26_351783	192	16	:	:	:
10_1101-2020_10_26_351783	192	17	bioRxiv	biorxiv	IN
10_1101-2020_10_26_351783	192	18	preprint	preprint	NN
10_1101-2020_10_26_351783	192	19	https://doi.org/10.1101/2020.10.26.351783	https://doi.org/10.1101/2020.10.26.351783	CC
10_1101-2020_10_26_351783	192	20	13	13	CD
10_1101-2020_10_26_351783	192	21	are	be	VBP
10_1101-2020_10_26_351783	192	22	significantly	significantly	RB
10_1101-2020_10_26_351783	192	23	involved	involve	VBN
10_1101-2020_10_26_351783	192	24	in	in	IN
10_1101-2020_10_26_351783	192	25	several	several	JJ
10_1101-2020_10_26_351783	192	26	inter	inter	NN
10_1101-2020_10_26_351783	192	27	-	-	JJ
10_1101-2020_10_26_351783	192	28	linked	link	VBN
10_1101-2020_10_26_351783	192	29	immune	immune	NN
10_1101-2020_10_26_351783	192	30	-	-	HYPH
10_1101-2020_10_26_351783	192	31	related	relate	VBN
10_1101-2020_10_26_351783	192	32	pathways	pathway	NNS
10_1101-2020_10_26_351783	192	33	,	,	,
10_1101-2020_10_26_351783	192	34	most	most	JJS
10_1101-2020_10_26_351783	192	35	of	of	IN
10_1101-2020_10_26_351783	192	36	which	which	WDT
10_1101-2020_10_26_351783	192	37	have	have	VBP
10_1101-2020_10_26_351783	192	38	been	be	VBN
10_1101-2020_10_26_351783	192	39	previously	previously	RB
10_1101-2020_10_26_351783	192	40	associated	associate	VBN
10_1101-2020_10_26_351783	192	41	to	to	IN
10_1101-2020_10_26_351783	192	42	MS	MS	NNP
10_1101-2020_10_26_351783	192	43	,	,	,
10_1101-2020_10_26_351783	192	44	including	include	VBG
10_1101-2020_10_26_351783	192	45	the	the	DT
10_1101-2020_10_26_351783	192	46	T	T	NNP
10_1101-2020_10_26_351783	192	47	cell	cell	NN
10_1101-2020_10_26_351783	192	48	receptor[25	receptor[25	NNP
10_1101-2020_10_26_351783	192	49	]	]	-RRB-
10_1101-2020_10_26_351783	192	50	(	(	-LRB-
10_1101-2020_10_26_351783	192	51	adjusted	adjust	VBN
10_1101-2020_10_26_351783	192	52	P	p	NN
10_1101-2020_10_26_351783	192	53	=	=	SYM
10_1101-2020_10_26_351783	192	54	3.6	3.6	CD
10_1101-2020_10_26_351783	192	55	x	x	SYM
10_1101-2020_10_26_351783	192	56	10	10	CD
10_1101-2020_10_26_351783	192	57	-	-	SYM
10_1101-2020_10_26_351783	192	58	47	47	CD
10_1101-2020_10_26_351783	192	59	)	)	-RRB-
10_1101-2020_10_26_351783	192	60	,	,	,
10_1101-2020_10_26_351783	192	61	PI3K	PI3K	NNP
10_1101-2020_10_26_351783	192	62	/	/	SYM
10_1101-2020_10_26_351783	192	63	Akt[26	Akt[26	NFP
10_1101-2020_10_26_351783	192	64	]	]	-RRB-
10_1101-2020_10_26_351783	192	65	(	(	-LRB-
10_1101-2020_10_26_351783	192	66	P	p	NN
10_1101-2020_10_26_351783	192	67	=	=	SYM
10_1101-2020_10_26_351783	192	68	4.6	4.6	CD
10_1101-2020_10_26_351783	192	69	x	x	SYM
10_1101-2020_10_26_351783	192	70	10	10	CD
10_1101-2020_10_26_351783	192	71	-	-	SYM
10_1101-2020_10_26_351783	192	72	35	35	CD
10_1101-2020_10_26_351783	192	73	)	)	-RRB-
10_1101-2020_10_26_351783	192	74	,	,	,
10_1101-2020_10_26_351783	192	75	ErbB[27	ErbB[27	VBN
10_1101-2020_10_26_351783	192	76	]	]	-RRB-
10_1101-2020_10_26_351783	192	77	(	(	-LRB-
10_1101-2020_10_26_351783	192	78	P	p	NN
10_1101-2020_10_26_351783	192	79	=	=	SYM
10_1101-2020_10_26_351783	192	80	7.7	7.7	CD
10_1101-2020_10_26_351783	192	81	x	x	SYM
10_1101-2020_10_26_351783	192	82	10	10	CD
10_1101-2020_10_26_351783	192	83	-	-	SYM
10_1101-2020_10_26_351783	192	84	32	32	CD
10_1101-2020_10_26_351783	192	85	)	)	-RRB-
10_1101-2020_10_26_351783	192	86	,	,	,
10_1101-2020_10_26_351783	192	87	Fc	Fc	NNP
10_1101-2020_10_26_351783	192	88	epsilon	epsilon	NN
10_1101-2020_10_26_351783	192	89	RI[28	RI[28	NNS
10_1101-2020_10_26_351783	192	90	]	]	-RRB-
10_1101-2020_10_26_351783	192	91	(	(	-LRB-
10_1101-2020_10_26_351783	192	92	P	p	NN
10_1101-2020_10_26_351783	192	93	=	=	SYM
10_1101-2020_10_26_351783	192	94	8.3	8.3	CD
10_1101-2020_10_26_351783	192	95	x	x	SYM
10_1101-2020_10_26_351783	192	96	10	10	CD
10_1101-2020_10_26_351783	192	97	-	-	SYM
10_1101-2020_10_26_351783	192	98	30	30	CD
10_1101-2020_10_26_351783	192	99	)	)	-RRB-
10_1101-2020_10_26_351783	192	100	,	,	,
10_1101-2020_10_26_351783	192	101	chemokine[29,30	chemokine[29,30	CD
10_1101-2020_10_26_351783	192	102	]	]	-RRB-
10_1101-2020_10_26_351783	192	103	(	(	-LRB-
10_1101-2020_10_26_351783	192	104	P	p	NN
10_1101-2020_10_26_351783	192	105	=	=	SYM
10_1101-2020_10_26_351783	192	106	2.6	2.6	CD
10_1101-2020_10_26_351783	192	107	x	x	SYM
10_1101-2020_10_26_351783	192	108	10	10	CD
10_1101-2020_10_26_351783	192	109	-	-	SYM
10_1101-2020_10_26_351783	192	110	28	28	CD
10_1101-2020_10_26_351783	192	111	)	)	-RRB-
10_1101-2020_10_26_351783	192	112	,	,	,
10_1101-2020_10_26_351783	192	113	MAPK[31,32	mapk[31,32	NN
10_1101-2020_10_26_351783	192	114	]	]	-RRB-
10_1101-2020_10_26_351783	192	115	(	(	-LRB-
10_1101-2020_10_26_351783	192	116	P	p	NN
10_1101-2020_10_26_351783	192	117	=	=	SYM
10_1101-2020_10_26_351783	192	118	2.0	2.0	CD
10_1101-2020_10_26_351783	192	119	x	x	SYM
10_1101-2020_10_26_351783	192	120	10	10	CD
10_1101-2020_10_26_351783	192	121	-	-	SYM
10_1101-2020_10_26_351783	192	122	25	25	CD
10_1101-2020_10_26_351783	192	123	)	)	-RRB-
10_1101-2020_10_26_351783	192	124	,	,	,
10_1101-2020_10_26_351783	192	125	and	and	CC
10_1101-2020_10_26_351783	192	126	B	b	NN
10_1101-2020_10_26_351783	192	127	cell	cell	NN
10_1101-2020_10_26_351783	192	128	receptor[32	receptor[32	NNP
10_1101-2020_10_26_351783	192	129	]	]	-RRB-
10_1101-2020_10_26_351783	192	130	(	(	-LRB-
10_1101-2020_10_26_351783	192	131	P	p	NN
10_1101-2020_10_26_351783	192	132	=	=	SYM
10_1101-2020_10_26_351783	192	133	3.9	3.9	CD
10_1101-2020_10_26_351783	192	134	x	x	SYM
10_1101-2020_10_26_351783	192	135	10	10	CD
10_1101-2020_10_26_351783	192	136	-	-	SYM
10_1101-2020_10_26_351783	192	137	19	19	CD
10_1101-2020_10_26_351783	192	138	)	)	-RRB-
10_1101-2020_10_26_351783	192	139	signaling	signal	VBG
10_1101-2020_10_26_351783	192	140	pathways	pathway	NNS
10_1101-2020_10_26_351783	192	141	;	;	,
10_1101-2020_10_26_351783	192	142	Th17	Th17	NNP
10_1101-2020_10_26_351783	192	143	(	(	-LRB-
10_1101-2020_10_26_351783	192	144	P	p	NN
10_1101-2020_10_26_351783	192	145	=	=	SYM
10_1101-2020_10_26_351783	192	146	9.6	9.6	CD
10_1101-2020_10_26_351783	192	147	x	x	SYM
10_1101-2020_10_26_351783	192	148	10	10	CD
10_1101-2020_10_26_351783	192	149	-	-	SYM
10_1101-2020_10_26_351783	192	150	29	29	CD
10_1101-2020_10_26_351783	192	151	)	)	-RRB-
10_1101-2020_10_26_351783	192	152	,	,	,
10_1101-2020_10_26_351783	192	153	and	and	CC
10_1101-2020_10_26_351783	192	154	Th1	th1	NN
10_1101-2020_10_26_351783	192	155	and	and	CC
10_1101-2020_10_26_351783	192	156	Th2	th2	NN
10_1101-2020_10_26_351783	192	157	(	(	-LRB-
10_1101-2020_10_26_351783	192	158	P	p	NN
10_1101-2020_10_26_351783	192	159	=	=	SYM
10_1101-2020_10_26_351783	192	160	6.9	6.9	CD
10_1101-2020_10_26_351783	192	161	x	x	SYM
10_1101-2020_10_26_351783	192	162	10	10	CD
10_1101-2020_10_26_351783	192	163	-	-	SYM
10_1101-2020_10_26_351783	192	164	19	19	CD
10_1101-2020_10_26_351783	192	165	)	)	-RRB-
10_1101-2020_10_26_351783	192	166	cell	cell	NN
10_1101-2020_10_26_351783	192	167	differentiation[33	differentiation[33	NN
10_1101-2020_10_26_351783	192	168	]	]	-RRB-
10_1101-2020_10_26_351783	192	169	;	;	,
10_1101-2020_10_26_351783	192	170	natural	natural	JJ
10_1101-2020_10_26_351783	192	171	killer	killer	NN
10_1101-2020_10_26_351783	192	172	cell	cell	NN
10_1101-2020_10_26_351783	192	173	mediated	mediate	VBD
10_1101-2020_10_26_351783	192	174	cytotoxicity	cytotoxicity	NN
10_1101-2020_10_26_351783	192	175	(	(	-LRB-
10_1101-2020_10_26_351783	192	176	P	p	NN
10_1101-2020_10_26_351783	192	177	=	=	SYM
10_1101-2020_10_26_351783	192	178	1.6	1.6	CD
10_1101-2020_10_26_351783	192	179	x	x	SYM
10_1101-2020_10_26_351783	192	180	10	10	CD
10_1101-2020_10_26_351783	192	181	-	-	SYM
10_1101-2020_10_26_351783	192	182	27	27	CD
10_1101-2020_10_26_351783	192	183	)	)	-RRB-
10_1101-2020_10_26_351783	192	184	;	;	:
10_1101-2020_10_26_351783	192	185	and	and	CC
10_1101-2020_10_26_351783	192	186	leukocyte	leukocyte	NN
10_1101-2020_10_26_351783	192	187	transendothelial	transendothelial	NN
10_1101-2020_10_26_351783	192	188	migration	migration	NN
10_1101-2020_10_26_351783	192	189	(	(	-LRB-
10_1101-2020_10_26_351783	192	190	P	p	NN
10_1101-2020_10_26_351783	192	191	=	=	SYM
10_1101-2020_10_26_351783	192	192	3.9	3.9	CD
10_1101-2020_10_26_351783	192	193	x	x	SYM
10_1101-2020_10_26_351783	192	194	10	10	CD
10_1101-2020_10_26_351783	192	195	-	-	SYM
10_1101-2020_10_26_351783	192	196	20	20	CD
10_1101-2020_10_26_351783	192	197	)	)	-RRB-
10_1101-2020_10_26_351783	192	198	,	,	,
10_1101-2020_10_26_351783	192	199	which	which	WDT
10_1101-2020_10_26_351783	192	200	indeed	indeed	RB
10_1101-2020_10_26_351783	192	201	supports	support	VBZ
10_1101-2020_10_26_351783	192	202	their	-PRON-	PRP$
10_1101-2020_10_26_351783	192	203	relevance	relevance	NN
10_1101-2020_10_26_351783	192	204	in	in	IN
10_1101-2020_10_26_351783	192	205	MS	MS	NNP
10_1101-2020_10_26_351783	192	206	.	.	.
10_1101-2020_10_26_351783	192	207	Interestingly	interestingly	RB
10_1101-2020_10_26_351783	192	208	,	,	,
10_1101-2020_10_26_351783	192	209	the	the	DT
10_1101-2020_10_26_351783	192	210	module	module	NN
10_1101-2020_10_26_351783	192	211	was	be	VBD
10_1101-2020_10_26_351783	192	212	also	also	RB
10_1101-2020_10_26_351783	192	213	highly	highly	RB
10_1101-2020_10_26_351783	192	214	enriched	enrich	VBN
10_1101-2020_10_26_351783	192	215	in	in	IN
10_1101-2020_10_26_351783	192	216	morphogenetic	morphogenetic	JJ
10_1101-2020_10_26_351783	192	217	and	and	CC
10_1101-2020_10_26_351783	192	218	neurogenetic	neurogenetic	JJ
10_1101-2020_10_26_351783	192	219	signaling	signaling	NN
10_1101-2020_10_26_351783	192	220	pathways	pathway	NNS
10_1101-2020_10_26_351783	192	221	,	,	,
10_1101-2020_10_26_351783	192	222	such	such	JJ
10_1101-2020_10_26_351783	192	223	as	as	IN
10_1101-2020_10_26_351783	192	224	the	the	DT
10_1101-2020_10_26_351783	192	225	neurotrophin	neurotrophin	NN
10_1101-2020_10_26_351783	192	226	(	(	-LRB-
10_1101-2020_10_26_351783	192	227	adjusted	adjust	VBN
10_1101-2020_10_26_351783	192	228	P	p	NN
10_1101-2020_10_26_351783	192	229	=	=	SYM
10_1101-2020_10_26_351783	192	230	1.3	1.3	CD
10_1101-2020_10_26_351783	192	231	x	x	SYM
10_1101-2020_10_26_351783	192	232	10	10	CD
10_1101-2020_10_26_351783	192	233	-	-	SYM
10_1101-2020_10_26_351783	192	234	36	36	CD
10_1101-2020_10_26_351783	192	235	)	)	-RRB-
10_1101-2020_10_26_351783	192	236	,	,	,
10_1101-2020_10_26_351783	192	237	Ras	Ras	NNP
10_1101-2020_10_26_351783	192	238	(	(	-LRB-
10_1101-2020_10_26_351783	192	239	P	p	NN
10_1101-2020_10_26_351783	192	240	=	=	SYM
10_1101-2020_10_26_351783	192	241	1.4	1.4	CD
10_1101-2020_10_26_351783	192	242	x	x	SYM
10_1101-2020_10_26_351783	192	243	10	10	CD
10_1101-2020_10_26_351783	192	244	-	-	SYM
10_1101-2020_10_26_351783	192	245	36	36	CD
10_1101-2020_10_26_351783	192	246	)	)	-RRB-
10_1101-2020_10_26_351783	192	247	,	,	,
10_1101-2020_10_26_351783	192	248	Rap1	Rap1	NNP
10_1101-2020_10_26_351783	192	249	(	(	-LRB-
10_1101-2020_10_26_351783	192	250	P	p	NN
10_1101-2020_10_26_351783	192	251	=	=	SYM
10_1101-2020_10_26_351783	192	252	2.2	2.2	CD
10_1101-2020_10_26_351783	192	253	x	x	SYM
10_1101-2020_10_26_351783	192	254	10	10	CD
10_1101-2020_10_26_351783	192	255	-	-	SYM
10_1101-2020_10_26_351783	192	256	35	35	CD
10_1101-2020_10_26_351783	192	257	)	)	-RRB-
10_1101-2020_10_26_351783	192	258	,	,	,
10_1101-2020_10_26_351783	192	259	vascular	vascular	JJ
10_1101-2020_10_26_351783	192	260	endothelial	endothelial	JJ
10_1101-2020_10_26_351783	192	261	growth	growth	NN
10_1101-2020_10_26_351783	192	262	factor	factor	NN
10_1101-2020_10_26_351783	192	263	(	(	-LRB-
10_1101-2020_10_26_351783	192	264	VEGF	vegf	NN
10_1101-2020_10_26_351783	192	265	,	,	,
10_1101-2020_10_26_351783	192	266	P	p	NN
10_1101-2020_10_26_351783	192	267	=	=	SYM
10_1101-2020_10_26_351783	192	268	1.7	1.7	CD
10_1101-2020_10_26_351783	192	269	x	x	SYM
10_1101-2020_10_26_351783	192	270	10	10	CD
10_1101-2020_10_26_351783	192	271	-	-	SYM
10_1101-2020_10_26_351783	192	272	27	27	CD
10_1101-2020_10_26_351783	192	273	)	)	-RRB-
10_1101-2020_10_26_351783	192	274	,	,	,
10_1101-2020_10_26_351783	192	275	FoxO	FoxO	NNP
10_1101-2020_10_26_351783	192	276	(	(	-LRB-
10_1101-2020_10_26_351783	192	277	P	p	NN
10_1101-2020_10_26_351783	192	278	=	=	SYM
10_1101-2020_10_26_351783	192	279	3.6	3.6	CD
10_1101-2020_10_26_351783	192	280	x	x	SYM
10_1101-2020_10_26_351783	192	281	10	10	CD
10_1101-2020_10_26_351783	192	282	-	-	SYM
10_1101-2020_10_26_351783	192	283	27	27	CD
10_1101-2020_10_26_351783	192	284	)	)	-RRB-
10_1101-2020_10_26_351783	192	285	,	,	,
10_1101-2020_10_26_351783	192	286	and	and	CC
10_1101-2020_10_26_351783	192	287	mTOR	mTOR	NNS
10_1101-2020_10_26_351783	192	288	(	(	-LRB-
10_1101-2020_10_26_351783	192	289	P	p	NN
10_1101-2020_10_26_351783	192	290	=	=	SYM
10_1101-2020_10_26_351783	192	291	4.1	4.1	CD
10_1101-2020_10_26_351783	192	292	x	x	SYM
10_1101-2020_10_26_351783	192	293	10	10	CD
10_1101-2020_10_26_351783	192	294	-	-	SYM
10_1101-2020_10_26_351783	192	295	14	14	CD
10_1101-2020_10_26_351783	192	296	)	)	-RRB-
10_1101-2020_10_26_351783	192	297	signaling	signal	VBG
10_1101-2020_10_26_351783	192	298	pathways	pathway	NNS
10_1101-2020_10_26_351783	192	299	;	;	,
10_1101-2020_10_26_351783	192	300	and	and	CC
10_1101-2020_10_26_351783	192	301	in	in	IN
10_1101-2020_10_26_351783	192	302	growth	growth	NN
10_1101-2020_10_26_351783	192	303	hormone	hormone	NN
10_1101-2020_10_26_351783	192	304	synthesis	synthesis	NN
10_1101-2020_10_26_351783	192	305	,	,	,
10_1101-2020_10_26_351783	192	306	secretion	secretion	NN
10_1101-2020_10_26_351783	192	307	and	and	CC
10_1101-2020_10_26_351783	192	308	action	action	NN
10_1101-2020_10_26_351783	192	309	(	(	-LRB-
10_1101-2020_10_26_351783	192	310	P	p	NN
10_1101-2020_10_26_351783	192	311	=	=	SYM
10_1101-2020_10_26_351783	192	312	6.6	6.6	CD
10_1101-2020_10_26_351783	192	313	x	x	SYM
10_1101-2020_10_26_351783	192	314	10	10	CD
10_1101-2020_10_26_351783	192	315	-	-	SYM
10_1101-2020_10_26_351783	192	316	31	31	CD
10_1101-2020_10_26_351783	192	317	)	)	-RRB-
10_1101-2020_10_26_351783	192	318	.	.	.
10_1101-2020_10_26_351783	193	1	The	the	DT
10_1101-2020_10_26_351783	193	2	multi	multi	JJ
10_1101-2020_10_26_351783	193	3	-	-	HYPH
10_1101-2020_10_26_351783	193	4	omics	omic	NNS
10_1101-2020_10_26_351783	193	5	MS	MS	NNP
10_1101-2020_10_26_351783	193	6	module	module	NN
10_1101-2020_10_26_351783	193	7	was	be	VBD
10_1101-2020_10_26_351783	193	8	enriched	enrich	VBN
10_1101-2020_10_26_351783	193	9	in	in	IN
10_1101-2020_10_26_351783	193	10	genes	gene	NNS
10_1101-2020_10_26_351783	193	11	associated	associate	VBN
10_1101-2020_10_26_351783	193	12	with	with	IN
10_1101-2020_10_26_351783	193	13	five	five	CD
10_1101-2020_10_26_351783	193	14	known	know	VBN
10_1101-2020_10_26_351783	193	15	environmental	environmental	JJ
10_1101-2020_10_26_351783	193	16	MS	MS	NNP
10_1101-2020_10_26_351783	193	17	risk	risk	NN
10_1101-2020_10_26_351783	193	18	factors	factor	NNS
10_1101-2020_10_26_351783	193	19	validated	validate	VBN
10_1101-2020_10_26_351783	193	20	in	in	IN
10_1101-2020_10_26_351783	193	21	an	an	DT
10_1101-2020_10_26_351783	193	22	independent	independent	JJ
10_1101-2020_10_26_351783	193	23	cohort	cohort	NN
10_1101-2020_10_26_351783	193	24	.	.	.
10_1101-2020_10_26_351783	194	1	Second	second	JJ
10_1101-2020_10_26_351783	194	2	,	,	,
10_1101-2020_10_26_351783	194	3	from	from	IN
10_1101-2020_10_26_351783	194	4	a	a	DT
10_1101-2020_10_26_351783	194	5	literature	literature	NN
10_1101-2020_10_26_351783	194	6	study[34,35	study[34,35	NNS
10_1101-2020_10_26_351783	194	7	]	]	-RRB-
10_1101-2020_10_26_351783	194	8	we	-PRON-	PRP
10_1101-2020_10_26_351783	194	9	found	find	VBD
10_1101-2020_10_26_351783	194	10	nine	nine	CD
10_1101-2020_10_26_351783	194	11	environmental	environmental	JJ
10_1101-2020_10_26_351783	194	12	MS	MS	NNP
10_1101-2020_10_26_351783	194	13	risk	risk	NN
10_1101-2020_10_26_351783	194	14	factors	factor	NNS
10_1101-2020_10_26_351783	194	15	of	of	IN
10_1101-2020_10_26_351783	194	16	varying	vary	VBG
10_1101-2020_10_26_351783	194	17	evidence	evidence	NN
10_1101-2020_10_26_351783	194	18	for	for	IN
10_1101-2020_10_26_351783	194	19	which	which	WDT
10_1101-2020_10_26_351783	194	20	we	-PRON-	PRP
10_1101-2020_10_26_351783	194	21	could	could	MD
10_1101-2020_10_26_351783	194	22	identify	identify	VB
10_1101-2020_10_26_351783	194	23	methylation	methylation	NN
10_1101-2020_10_26_351783	194	24	studies	study	NNS
10_1101-2020_10_26_351783	194	25	in	in	IN
10_1101-2020_10_26_351783	194	26	healthy	healthy	JJ
10_1101-2020_10_26_351783	194	27	controls	control	NNS
10_1101-2020_10_26_351783	194	28	.	.	.
10_1101-2020_10_26_351783	195	1	For	for	IN
10_1101-2020_10_26_351783	195	2	each	each	DT
10_1101-2020_10_26_351783	195	3	of	of	IN
10_1101-2020_10_26_351783	195	4	these	these	DT
10_1101-2020_10_26_351783	195	5	risk	risk	NN
10_1101-2020_10_26_351783	195	6	factors	factor	NNS
10_1101-2020_10_26_351783	195	7	we	-PRON-	PRP
10_1101-2020_10_26_351783	195	8	derived	derive	VBD
10_1101-2020_10_26_351783	195	9	the	the	DT
10_1101-2020_10_26_351783	195	10	top	top	JJ
10_1101-2020_10_26_351783	195	11	1000	1000	CD
10_1101-2020_10_26_351783	195	12	differentially	differentially	RB
10_1101-2020_10_26_351783	195	13	methylated	methylate	VBN
10_1101-2020_10_26_351783	195	14	genes	gene	NNS
10_1101-2020_10_26_351783	195	15	(	(	-LRB-
10_1101-2020_10_26_351783	195	16	DMGs	DMGs	NNP
10_1101-2020_10_26_351783	195	17	)	)	-RRB-
10_1101-2020_10_26_351783	195	18	and	and	CC
10_1101-2020_10_26_351783	195	19	tested	test	VBD
10_1101-2020_10_26_351783	195	20	their	-PRON-	PRP$
10_1101-2020_10_26_351783	195	21	enrichment	enrichment	NN
10_1101-2020_10_26_351783	195	22	with	with	IN
10_1101-2020_10_26_351783	195	23	the	the	DT
10_1101-2020_10_26_351783	195	24	module	module	NN
10_1101-2020_10_26_351783	195	25	.	.	.
10_1101-2020_10_26_351783	196	1	Intriguingly	intriguingly	RB
10_1101-2020_10_26_351783	196	2	,	,	,
10_1101-2020_10_26_351783	196	3	the	the	DT
10_1101-2020_10_26_351783	196	4	module	module	NN
10_1101-2020_10_26_351783	196	5	was	be	VBD
10_1101-2020_10_26_351783	196	6	significantly	significantly	RB
10_1101-2020_10_26_351783	196	7	enriched	enrich	VBN
10_1101-2020_10_26_351783	196	8	for	for	IN
10_1101-2020_10_26_351783	196	9	genes	gene	NNS
10_1101-2020_10_26_351783	196	10	associated	associate	VBN
10_1101-2020_10_26_351783	196	11	with	with	IN
10_1101-2020_10_26_351783	196	12	five	five	CD
10_1101-2020_10_26_351783	196	13	risk	risk	NN
10_1101-2020_10_26_351783	196	14	factors	factor	NNS
10_1101-2020_10_26_351783	196	15	(	(	-LRB-
10_1101-2020_10_26_351783	196	16	Fig	fig	NN
10_1101-2020_10_26_351783	196	17	.	.	.
10_1101-2020_10_26_351783	197	1	5b	5b	NN
10_1101-2020_10_26_351783	197	2	)	)	-RRB-
10_1101-2020_10_26_351783	197	3	,	,	,
10_1101-2020_10_26_351783	197	4	which	which	WDT
10_1101-2020_10_26_351783	197	5	included	include	VBD
10_1101-2020_10_26_351783	197	6	the	the	DT
10_1101-2020_10_26_351783	197	7	top	top	JJ
10_1101-2020_10_26_351783	197	8	associated	associate	VBN
10_1101-2020_10_26_351783	197	9	risk	risk	NN
10_1101-2020_10_26_351783	197	10	factors	factor	NNS
10_1101-2020_10_26_351783	197	11	,	,	,
10_1101-2020_10_26_351783	197	12	i.e.	i.e.	FW
10_1101-2020_10_26_351783	197	13	,	,	,
10_1101-2020_10_26_351783	197	14	Epstein	Epstein	NNP
10_1101-2020_10_26_351783	197	15	-	-	HYPH
10_1101-2020_10_26_351783	197	16	Barr	Barr	NNP
10_1101-2020_10_26_351783	197	17	virus	virus	NN
10_1101-2020_10_26_351783	197	18	(	(	-LRB-
10_1101-2020_10_26_351783	197	19	EBV	EBV	NNP
10_1101-2020_10_26_351783	197	20	)	)	-RRB-
10_1101-2020_10_26_351783	197	21	infection	infection	NN
10_1101-2020_10_26_351783	197	22	(	(	-LRB-
10_1101-2020_10_26_351783	197	23	Fisher	Fisher	NNP
10_1101-2020_10_26_351783	197	24	exact	exact	JJ
10_1101-2020_10_26_351783	197	25	test	test	NN
10_1101-2020_10_26_351783	197	26	P	p	NN
10_1101-2020_10_26_351783	197	27	=	=	SYM
10_1101-2020_10_26_351783	197	28	1.5	1.5	CD
10_1101-2020_10_26_351783	197	29	x	x	SYM
10_1101-2020_10_26_351783	197	30	10	10	CD
10_1101-2020_10_26_351783	197	31	-	-	SYM
10_1101-2020_10_26_351783	197	32	3	3	CD
10_1101-2020_10_26_351783	197	33	,	,	,
10_1101-2020_10_26_351783	197	34	OR	or	CC
10_1101-2020_10_26_351783	197	35	=	=	SYM
10_1101-2020_10_26_351783	197	36	2.1	2.1	CD
10_1101-2020_10_26_351783	197	37	)	)	-RRB-
10_1101-2020_10_26_351783	197	38	and	and	CC
10_1101-2020_10_26_351783	197	39	smoking	smoking	NN
10_1101-2020_10_26_351783	197	40	(	(	-LRB-
10_1101-2020_10_26_351783	197	41	P	p	NN
10_1101-2020_10_26_351783	197	42	=	=	SYM
10_1101-2020_10_26_351783	197	43	1.2	1.2	CD
10_1101-2020_10_26_351783	197	44	x	x	SYM
10_1101-2020_10_26_351783	197	45	10	10	CD
10_1101-2020_10_26_351783	197	46	-	-	SYM
10_1101-2020_10_26_351783	197	47	4	4	CD
10_1101-2020_10_26_351783	197	48	,	,	,
10_1101-2020_10_26_351783	197	49	OR	or	CC
10_1101-2020_10_26_351783	197	50	=	=	SYM
10_1101-2020_10_26_351783	197	51	2.3	2.3	CD
10_1101-2020_10_26_351783	197	52	)	)	-RRB-
10_1101-2020_10_26_351783	197	53	,	,	,
10_1101-2020_10_26_351783	197	54	as	as	RB
10_1101-2020_10_26_351783	197	55	well	well	RB
10_1101-2020_10_26_351783	197	56	as	as	IN
10_1101-2020_10_26_351783	197	57	low	low	JJ
10_1101-2020_10_26_351783	197	58	sun	sun	NN
10_1101-2020_10_26_351783	197	59	exposure	exposure	NN
10_1101-2020_10_26_351783	197	60	(	(	-LRB-
10_1101-2020_10_26_351783	197	61	P	p	NN
10_1101-2020_10_26_351783	197	62	=	=	SYM
10_1101-2020_10_26_351783	197	63	1.2	1.2	CD
10_1101-2020_10_26_351783	197	64	x	x	SYM
10_1101-2020_10_26_351783	197	65	10	10	CD
10_1101-2020_10_26_351783	197	66	-	-	SYM
10_1101-2020_10_26_351783	197	67	4	4	CD
10_1101-2020_10_26_351783	197	68	,	,	,
10_1101-2020_10_26_351783	197	69	OR	or	CC
10_1101-2020_10_26_351783	197	70	=	=	SYM
10_1101-2020_10_26_351783	197	71	2.3	2.3	CD
10_1101-2020_10_26_351783	197	72	)	)	-RRB-
10_1101-2020_10_26_351783	197	73	,	,	,
10_1101-2020_10_26_351783	197	74	high	high	JJ
10_1101-2020_10_26_351783	197	75	BMI	BMI	NNP
10_1101-2020_10_26_351783	197	76	(	(	-LRB-
10_1101-2020_10_26_351783	197	77	P	p	NN
10_1101-2020_10_26_351783	197	78	=	=	SYM
10_1101-2020_10_26_351783	197	79	0.023	0.023	CD
10_1101-2020_10_26_351783	197	80	,	,	,
10_1101-2020_10_26_351783	197	81	OR	or	CC
10_1101-2020_10_26_351783	197	82	=	=	SYM
10_1101-2020_10_26_351783	197	83	1.7	1.7	CD
10_1101-2020_10_26_351783	197	84	)	)	-RRB-
10_1101-2020_10_26_351783	197	85	and	and	CC
10_1101-2020_10_26_351783	197	86	alcohol	alcohol	NN
10_1101-2020_10_26_351783	197	87	consumption	consumption	NN
10_1101-2020_10_26_351783	197	88	(	(	-LRB-
10_1101-2020_10_26_351783	197	89	P	p	NN
10_1101-2020_10_26_351783	197	90	=	=	SYM
10_1101-2020_10_26_351783	197	91	2.9	2.9	CD
10_1101-2020_10_26_351783	197	92	x	x	SYM
10_1101-2020_10_26_351783	197	93	10	10	CD
10_1101-2020_10_26_351783	197	94	-	-	SYM
10_1101-2020_10_26_351783	197	95	4	4	CD
10_1101-2020_10_26_351783	197	96	,	,	,
10_1101-2020_10_26_351783	197	97	OR	or	CC
10_1101-2020_10_26_351783	197	98	=	=	SYM
10_1101-2020_10_26_351783	197	99	2.2	2.2	CD
10_1101-2020_10_26_351783	197	100	)	)	-RRB-
10_1101-2020_10_26_351783	197	101	.	.	.
10_1101-2020_10_26_351783	198	1	Then	then	RB
10_1101-2020_10_26_351783	198	2	,	,	,
10_1101-2020_10_26_351783	198	3	we	-PRON-	PRP
10_1101-2020_10_26_351783	198	4	asked	ask	VBD
10_1101-2020_10_26_351783	198	5	whether	whether	IN
10_1101-2020_10_26_351783	198	6	these	these	DT
10_1101-2020_10_26_351783	198	7	putative	putative	JJ
10_1101-2020_10_26_351783	198	8	gene	gene	NN
10_1101-2020_10_26_351783	198	9	-	-	HYPH
10_1101-2020_10_26_351783	198	10	risk	risk	NN
10_1101-2020_10_26_351783	198	11	factor	factor	NN
10_1101-2020_10_26_351783	198	12	associations	association	NNS
10_1101-2020_10_26_351783	198	13	could	could	MD
10_1101-2020_10_26_351783	198	14	be	be	VB
10_1101-2020_10_26_351783	198	15	validated	validate	VBN
10_1101-2020_10_26_351783	198	16	using	use	VBG
10_1101-2020_10_26_351783	198	17	an	an	DT
10_1101-2020_10_26_351783	198	18	independent	independent	JJ
10_1101-2020_10_26_351783	198	19	omics	omic	NNS
10_1101-2020_10_26_351783	198	20	dataset	dataset	VBN
10_1101-2020_10_26_351783	198	21	with	with	IN
10_1101-2020_10_26_351783	198	22	paired	pair	VBN
10_1101-2020_10_26_351783	198	23	risk	risk	NN
10_1101-2020_10_26_351783	198	24	factor	factor	NN
10_1101-2020_10_26_351783	198	25	associations	association	NNS
10_1101-2020_10_26_351783	198	26	.	.	.
10_1101-2020_10_26_351783	199	1	For	for	IN
10_1101-2020_10_26_351783	199	2	this	this	DT
10_1101-2020_10_26_351783	199	3	purpose	purpose	NN
10_1101-2020_10_26_351783	199	4	,	,	,
10_1101-2020_10_26_351783	199	5	we	-PRON-	PRP
10_1101-2020_10_26_351783	199	6	utilized	utilize	VBD
10_1101-2020_10_26_351783	199	7	methylation	methylation	NN
10_1101-2020_10_26_351783	199	8	arrays	array	NNS
10_1101-2020_10_26_351783	199	9	of	of	IN
10_1101-2020_10_26_351783	199	10	peripheral	peripheral	JJ
10_1101-2020_10_26_351783	199	11	blood	blood	NN
10_1101-2020_10_26_351783	199	12	from	from	IN
10_1101-2020_10_26_351783	199	13	139	139	CD
10_1101-2020_10_26_351783	199	14	MS	MS	NNP
10_1101-2020_10_26_351783	199	15	patients	patient	NNS
10_1101-2020_10_26_351783	199	16	and	and	CC
10_1101-2020_10_26_351783	199	17	140	140	CD
10_1101-2020_10_26_351783	199	18	controls	control	NNS
10_1101-2020_10_26_351783	199	19	,	,	,
10_1101-2020_10_26_351783	199	20	which	which	WDT
10_1101-2020_10_26_351783	199	21	have	have	VBP
10_1101-2020_10_26_351783	199	22	been	be	VBN
10_1101-2020_10_26_351783	199	23	described	describe	VBN
10_1101-2020_10_26_351783	199	24	previously[36	previously[36	NNP
10_1101-2020_10_26_351783	199	25	]	]	-RRB-
10_1101-2020_10_26_351783	199	26	.	.	.
10_1101-2020_10_26_351783	200	1	In	in	IN
10_1101-2020_10_26_351783	200	2	this	this	DT
10_1101-2020_10_26_351783	200	3	analysis	analysis	NN
10_1101-2020_10_26_351783	200	4	we	-PRON-	PRP
10_1101-2020_10_26_351783	200	5	also	also	RB
10_1101-2020_10_26_351783	200	6	considered	consider	VBD
10_1101-2020_10_26_351783	200	7	risk	risk	NN
10_1101-2020_10_26_351783	200	8	factor	factor	NN
10_1101-2020_10_26_351783	200	9	associations	association	NNS
10_1101-2020_10_26_351783	200	10	for	for	IN
10_1101-2020_10_26_351783	200	11	each	each	DT
10_1101-2020_10_26_351783	200	12	individual	individual	NN
10_1101-2020_10_26_351783	200	13	including	include	VBG
10_1101-2020_10_26_351783	200	14	age	age	NN
10_1101-2020_10_26_351783	200	15	,	,	,
10_1101-2020_10_26_351783	200	16	sex	sex	NN
10_1101-2020_10_26_351783	200	17	,	,	,
10_1101-2020_10_26_351783	200	18	BMI	BMI	NNP
10_1101-2020_10_26_351783	200	19	at	at	IN
10_1101-2020_10_26_351783	200	20	age	age	NN
10_1101-2020_10_26_351783	200	21	of	of	IN
10_1101-2020_10_26_351783	200	22	20	20	CD
10_1101-2020_10_26_351783	200	23	,	,	,
10_1101-2020_10_26_351783	200	24	smoking	smoking	NN
10_1101-2020_10_26_351783	200	25	,	,	,
10_1101-2020_10_26_351783	200	26	alcohol	alcohol	NN
10_1101-2020_10_26_351783	200	27	consumption	consumption	NN
10_1101-2020_10_26_351783	200	28	,	,	,
10_1101-2020_10_26_351783	200	29	sun	sun	NN
10_1101-2020_10_26_351783	200	30	exposure	exposure	NN
10_1101-2020_10_26_351783	200	31	,	,	,
10_1101-2020_10_26_351783	200	32	night	night	NN
10_1101-2020_10_26_351783	200	33	shift	shift	NN
10_1101-2020_10_26_351783	200	34	work	work	NN
10_1101-2020_10_26_351783	200	35	,	,	,
10_1101-2020_10_26_351783	200	36	contact	contact	NN
10_1101-2020_10_26_351783	200	37	with	with	IN
10_1101-2020_10_26_351783	200	38	organic	organic	JJ
10_1101-2020_10_26_351783	200	39	solvents	solvent	NNS
10_1101-2020_10_26_351783	200	40	.	.	.
10_1101-2020_10_26_351783	201	1	This	this	DT
10_1101-2020_10_26_351783	201	2	enabled	enable	VBD
10_1101-2020_10_26_351783	201	3	analysis	analysis	NN
10_1101-2020_10_26_351783	201	4	of	of	IN
10_1101-2020_10_26_351783	201	5	DMGs	DMGs	NNPS
10_1101-2020_10_26_351783	201	6	for	for	IN
10_1101-2020_10_26_351783	201	7	the	the	DT
10_1101-2020_10_26_351783	201	8	MS	MS	NNP
10_1101-2020_10_26_351783	201	9	and	and	CC
10_1101-2020_10_26_351783	201	10	risk	risk	NN
10_1101-2020_10_26_351783	201	11	factor	factor	NN
10_1101-2020_10_26_351783	201	12	status	status	NN
10_1101-2020_10_26_351783	201	13	as	as	IN
10_1101-2020_10_26_351783	201	14	covariates	covariate	NNS
10_1101-2020_10_26_351783	201	15	in	in	IN
10_1101-2020_10_26_351783	201	16	linear	linear	JJ
10_1101-2020_10_26_351783	201	17	mixed	mixed	JJ
10_1101-2020_10_26_351783	201	18	effect	effect	NN
10_1101-2020_10_26_351783	201	19	(	(	-LRB-
10_1101-2020_10_26_351783	201	20	which	which	WDT
10_1101-2020_10_26_351783	201	21	was	be	VBD
10_1101-2020_10_26_351783	201	22	not	not	RB
10_1101-2020_10_26_351783	201	23	certified	certify	VBN
10_1101-2020_10_26_351783	201	24	by	by	IN
10_1101-2020_10_26_351783	201	25	peer	peer	NN
10_1101-2020_10_26_351783	201	26	review	review	NN
10_1101-2020_10_26_351783	201	27	)	)	-RRB-
10_1101-2020_10_26_351783	201	28	is	be	VBZ
10_1101-2020_10_26_351783	201	29	the	the	DT
10_1101-2020_10_26_351783	201	30	author	author	NN
10_1101-2020_10_26_351783	201	31	/	/	SYM
10_1101-2020_10_26_351783	201	32	funder	funder	NN
10_1101-2020_10_26_351783	201	33	.	.	.
10_1101-2020_10_26_351783	202	1	All	all	DT
10_1101-2020_10_26_351783	202	2	rights	right	NNS
10_1101-2020_10_26_351783	202	3	reserved	reserve	VBD
10_1101-2020_10_26_351783	202	4	.	.	.
10_1101-2020_10_26_351783	203	1	No	no	DT
10_1101-2020_10_26_351783	203	2	reuse	reuse	NN
10_1101-2020_10_26_351783	203	3	allowed	allow	VBN
10_1101-2020_10_26_351783	203	4	without	without	IN
10_1101-2020_10_26_351783	203	5	permission	permission	NN
10_1101-2020_10_26_351783	203	6	.	.	.
10_1101-2020_10_26_351783	204	1	The	the	DT
10_1101-2020_10_26_351783	204	2	copyright	copyright	NN
10_1101-2020_10_26_351783	204	3	holder	holder	NN
10_1101-2020_10_26_351783	204	4	for	for	IN
10_1101-2020_10_26_351783	204	5	this	this	DT
10_1101-2020_10_26_351783	204	6	preprintthis	preprintthis	NN
10_1101-2020_10_26_351783	204	7	version	version	NN
10_1101-2020_10_26_351783	204	8	posted	post	VBD
10_1101-2020_10_26_351783	204	9	January	January	NNP
10_1101-2020_10_26_351783	204	10	6	6	CD
10_1101-2020_10_26_351783	204	11	,	,	,
10_1101-2020_10_26_351783	204	12	2021	2021	CD
10_1101-2020_10_26_351783	204	13	.	.	.
10_1101-2020_10_26_351783	204	14	;	;	:
10_1101-2020_10_26_351783	204	15	https://doi.org/10.1101/2020.10.26.351783doi	https://doi.org/10.1101/2020.10.26.351783doi	ADD
10_1101-2020_10_26_351783	204	16	:	:	:
10_1101-2020_10_26_351783	204	17	bioRxiv	biorxiv	IN
10_1101-2020_10_26_351783	204	18	preprint	preprint	NN
10_1101-2020_10_26_351783	204	19	https://doi.org/10.1101/2020.10.26.351783	https://doi.org/10.1101/2020.10.26.351783	NNP
10_1101-2020_10_26_351783	204	20	14	14	CD
10_1101-2020_10_26_351783	204	21	analysis	analysis	NN
10_1101-2020_10_26_351783	204	22	.	.	.
10_1101-2020_10_26_351783	205	1	Indeed	indeed	RB
10_1101-2020_10_26_351783	205	2	,	,	,
10_1101-2020_10_26_351783	205	3	the	the	DT
10_1101-2020_10_26_351783	205	4	module	module	JJ
10_1101-2020_10_26_351783	205	5	genes	gene	NNS
10_1101-2020_10_26_351783	205	6	were	be	VBD
10_1101-2020_10_26_351783	205	7	highly	highly	RB
10_1101-2020_10_26_351783	205	8	significantly	significantly	RB
10_1101-2020_10_26_351783	205	9	enriched	enrich	VBN
10_1101-2020_10_26_351783	205	10	for	for	IN
10_1101-2020_10_26_351783	205	11	MS	MS	NNP
10_1101-2020_10_26_351783	205	12	(	(	-LRB-
10_1101-2020_10_26_351783	205	13	n	n	NNP
10_1101-2020_10_26_351783	205	14	=	=	SYM
10_1101-2020_10_26_351783	205	15	217	217	CD
10_1101-2020_10_26_351783	205	16	;	;	:
10_1101-2020_10_26_351783	205	17	permutation	permutation	NN
10_1101-2020_10_26_351783	205	18	test	test	NN
10_1101-2020_10_26_351783	205	19	P	p	NN
10_1101-2020_10_26_351783	205	20	=	=	SYM
10_1101-2020_10_26_351783	205	21	1.2	1.2	CD
10_1101-2020_10_26_351783	205	22	x	x	SYM
10_1101-2020_10_26_351783	205	23	10	10	CD
10_1101-2020_10_26_351783	205	24	-	-	SYM
10_1101-2020_10_26_351783	205	25	47	47	CD
10_1101-2020_10_26_351783	205	26	)	)	-RRB-
10_1101-2020_10_26_351783	205	27	,	,	,
10_1101-2020_10_26_351783	205	28	but	but	CC
10_1101-2020_10_26_351783	205	29	also	also	RB
10_1101-2020_10_26_351783	205	30	for	for	IN
10_1101-2020_10_26_351783	205	31	all	all	PDT
10_1101-2020_10_26_351783	205	32	the	the	DT
10_1101-2020_10_26_351783	205	33	tested	test	VBN
10_1101-2020_10_26_351783	205	34	risk	risk	NN
10_1101-2020_10_26_351783	205	35	factors	factor	NNS
10_1101-2020_10_26_351783	205	36	(	(	-LRB-
10_1101-2020_10_26_351783	205	37	EBV	EBV	NNP
10_1101-2020_10_26_351783	205	38	was	be	VBD
10_1101-2020_10_26_351783	205	39	not	not	RB
10_1101-2020_10_26_351783	205	40	included	include	VBN
10_1101-2020_10_26_351783	205	41	,	,	,
10_1101-2020_10_26_351783	205	42	Methods	Methods	NNP
10_1101-2020_10_26_351783	205	43	)	)	-RRB-
10_1101-2020_10_26_351783	205	44	and	and	CC
10_1101-2020_10_26_351783	205	45	non-	non-	NNP
10_1101-2020_10_26_351783	205	46	significantly	significantly	RB
10_1101-2020_10_26_351783	205	47	associated	associate	VBN
10_1101-2020_10_26_351783	205	48	to	to	IN
10_1101-2020_10_26_351783	205	49	age	age	NN
10_1101-2020_10_26_351783	205	50	and	and	CC
10_1101-2020_10_26_351783	205	51	sex	sex	NN
10_1101-2020_10_26_351783	205	52	having	have	VBG
10_1101-2020_10_26_351783	205	53	104	104	CD
10_1101-2020_10_26_351783	205	54	-	-	SYM
10_1101-2020_10_26_351783	205	55	135	135	CD
10_1101-2020_10_26_351783	205	56	of	of	IN
10_1101-2020_10_26_351783	205	57	the	the	DT
10_1101-2020_10_26_351783	205	58	genes	gene	NNS
10_1101-2020_10_26_351783	205	59	in	in	IN
10_1101-2020_10_26_351783	205	60	each	each	DT
10_1101-2020_10_26_351783	205	61	factor	factor	NN
10_1101-2020_10_26_351783	205	62	(	(	-LRB-
10_1101-2020_10_26_351783	205	63	3.9x10	3.9x10	CD
10_1101-2020_10_26_351783	205	64	-	-	SYM
10_1101-2020_10_26_351783	205	65	8	8	CD
10_1101-2020_10_26_351783	205	66	<	<	XX
10_1101-2020_10_26_351783	205	67	P	p	NN
10_1101-2020_10_26_351783	205	68	<	<	XX
10_1101-2020_10_26_351783	205	69	0.013	0.013	CD
10_1101-2020_10_26_351783	205	70	;	;	:
10_1101-2020_10_26_351783	205	71	Fig	fig	VB
10_1101-2020_10_26_351783	205	72	5b	5b	NN
10_1101-2020_10_26_351783	205	73	)	)	-RRB-
10_1101-2020_10_26_351783	205	74	.	.	.
10_1101-2020_10_26_351783	206	1	Combining	combine	VBG
10_1101-2020_10_26_351783	206	2	all	all	PDT
10_1101-2020_10_26_351783	206	3	these	these	DT
10_1101-2020_10_26_351783	206	4	results	result	NNS
10_1101-2020_10_26_351783	206	5	we	-PRON-	PRP
10_1101-2020_10_26_351783	206	6	found	find	VBD
10_1101-2020_10_26_351783	206	7	90	90	CD
10_1101-2020_10_26_351783	206	8	of	of	IN
10_1101-2020_10_26_351783	206	9	the	the	DT
10_1101-2020_10_26_351783	206	10	220	220	CD
10_1101-2020_10_26_351783	206	11	module	module	JJ
10_1101-2020_10_26_351783	206	12	genes	gene	NNS
10_1101-2020_10_26_351783	206	13	to	to	TO
10_1101-2020_10_26_351783	206	14	be	be	VB
10_1101-2020_10_26_351783	206	15	associated	associate	VBN
10_1101-2020_10_26_351783	206	16	with	with	IN
10_1101-2020_10_26_351783	206	17	a	a	DT
10_1101-2020_10_26_351783	206	18	risk	risk	NN
10_1101-2020_10_26_351783	206	19	factors	factor	NNS
10_1101-2020_10_26_351783	206	20	from	from	IN
10_1101-2020_10_26_351783	206	21	both	both	CC
10_1101-2020_10_26_351783	206	22	the	the	DT
10_1101-2020_10_26_351783	206	23	risk	risk	NN
10_1101-2020_10_26_351783	206	24	factor	factor	NN
10_1101-2020_10_26_351783	206	25	studies	study	NNS
10_1101-2020_10_26_351783	206	26	,	,	,
10_1101-2020_10_26_351783	206	27	25	25	CD
10_1101-2020_10_26_351783	206	28	genes	gene	NNS
10_1101-2020_10_26_351783	206	29	were	be	VBD
10_1101-2020_10_26_351783	206	30	associated	associate	VBN
10_1101-2020_10_26_351783	206	31	with	with	IN
10_1101-2020_10_26_351783	206	32	two	two	CD
10_1101-2020_10_26_351783	206	33	risk	risk	NN
10_1101-2020_10_26_351783	206	34	factors	factor	NNS
10_1101-2020_10_26_351783	206	35	,	,	,
10_1101-2020_10_26_351783	206	36	and	and	CC
10_1101-2020_10_26_351783	206	37	seven	seven	CD
10_1101-2020_10_26_351783	206	38	genes	gene	NNS
10_1101-2020_10_26_351783	206	39	were	be	VBD
10_1101-2020_10_26_351783	206	40	associated	associate	VBN
10_1101-2020_10_26_351783	206	41	with	with	IN
10_1101-2020_10_26_351783	206	42	three	three	CD
10_1101-2020_10_26_351783	206	43	risk	risk	NN
10_1101-2020_10_26_351783	206	44	factors	factor	NNS
10_1101-2020_10_26_351783	206	45	(	(	-LRB-
10_1101-2020_10_26_351783	206	46	CSK	CSK	NNP
10_1101-2020_10_26_351783	206	47	,	,	,
10_1101-2020_10_26_351783	206	48	PRKCA	PRKCA	NNP
10_1101-2020_10_26_351783	206	49	,	,	,
10_1101-2020_10_26_351783	206	50	PRKCZ	PRKCZ	NNP
10_1101-2020_10_26_351783	206	51	,	,	,
10_1101-2020_10_26_351783	206	52	RUNX1	runx1	NN
10_1101-2020_10_26_351783	206	53	,	,	,
10_1101-2020_10_26_351783	206	54	RUNX3	runx3	NN
10_1101-2020_10_26_351783	206	55	,	,	,
10_1101-2020_10_26_351783	206	56	STAT5A	STAT5A	NNP
10_1101-2020_10_26_351783	206	57	,	,	,
10_1101-2020_10_26_351783	206	58	and	and	CC
10_1101-2020_10_26_351783	206	59	SYNJ2	synj2	NN
10_1101-2020_10_26_351783	206	60	)	)	-RRB-
10_1101-2020_10_26_351783	206	61	(	(	-LRB-
10_1101-2020_10_26_351783	206	62	Fig	fig	NN
10_1101-2020_10_26_351783	206	63	.	.	.
10_1101-2020_10_26_351783	207	1	5c	5c	LS
10_1101-2020_10_26_351783	207	2	)	)	-RRB-
10_1101-2020_10_26_351783	207	3	.	.	.
10_1101-2020_10_26_351783	208	1	These	these	DT
10_1101-2020_10_26_351783	208	2	associations	association	NNS
10_1101-2020_10_26_351783	208	3	suggest	suggest	VBP
10_1101-2020_10_26_351783	208	4	that	that	IN
10_1101-2020_10_26_351783	208	5	the	the	DT
10_1101-2020_10_26_351783	208	6	multi	multi	JJ
10_1101-2020_10_26_351783	208	7	-	-	JJ
10_1101-2020_10_26_351783	208	8	omics	omics	JJ
10_1101-2020_10_26_351783	208	9	module	module	NN
10_1101-2020_10_26_351783	208	10	is	be	VBZ
10_1101-2020_10_26_351783	208	11	capturing	capture	VBG
10_1101-2020_10_26_351783	208	12	a	a	DT
10_1101-2020_10_26_351783	208	13	key	key	JJ
10_1101-2020_10_26_351783	208	14	disease	disease	NN
10_1101-2020_10_26_351783	208	15	network	network	NN
10_1101-2020_10_26_351783	208	16	with	with	IN
10_1101-2020_10_26_351783	208	17	both	both	CC
10_1101-2020_10_26_351783	208	18	genetically	genetically	RB
10_1101-2020_10_26_351783	208	19	and	and	CC
10_1101-2020_10_26_351783	208	20	epigenetically	epigenetically	RB
10_1101-2020_10_26_351783	208	21	driven	drive	VBN
10_1101-2020_10_26_351783	208	22	alterations	alteration	NNS
10_1101-2020_10_26_351783	208	23	,	,	,
10_1101-2020_10_26_351783	208	24	thereby	thereby	RB
10_1101-2020_10_26_351783	208	25	providing	provide	VBG
10_1101-2020_10_26_351783	208	26	the	the	DT
10_1101-2020_10_26_351783	208	27	possibility	possibility	NN
10_1101-2020_10_26_351783	208	28	to	to	TO
10_1101-2020_10_26_351783	208	29	use	use	VB
10_1101-2020_10_26_351783	208	30	it	-PRON-	PRP
10_1101-2020_10_26_351783	208	31	to	to	TO
10_1101-2020_10_26_351783	208	32	identify	identify	VB
10_1101-2020_10_26_351783	208	33	potential	potential	JJ
10_1101-2020_10_26_351783	208	34	novel	novel	JJ
10_1101-2020_10_26_351783	208	35	biomarkers	biomarker	NNS
10_1101-2020_10_26_351783	208	36	or	or	CC
10_1101-2020_10_26_351783	208	37	therapeutic	therapeutic	JJ
10_1101-2020_10_26_351783	208	38	targets	target	NNS
10_1101-2020_10_26_351783	208	39	for	for	IN
10_1101-2020_10_26_351783	208	40	MS	MS	NNP
10_1101-2020_10_26_351783	208	41	.	.	.
10_1101-2020_10_26_351783	208	42	�	�	NNP
10_1101-2020_10_26_351783	208	43	(	(	-LRB-
10_1101-2020_10_26_351783	208	44	which	which	WDT
10_1101-2020_10_26_351783	208	45	was	be	VBD
10_1101-2020_10_26_351783	208	46	not	not	RB
10_1101-2020_10_26_351783	208	47	certified	certify	VBN
10_1101-2020_10_26_351783	208	48	by	by	IN
10_1101-2020_10_26_351783	208	49	peer	peer	NN
10_1101-2020_10_26_351783	208	50	review	review	NN
10_1101-2020_10_26_351783	208	51	)	)	-RRB-
10_1101-2020_10_26_351783	208	52	is	be	VBZ
10_1101-2020_10_26_351783	208	53	the	the	DT
10_1101-2020_10_26_351783	208	54	author	author	NN
10_1101-2020_10_26_351783	208	55	/	/	SYM
10_1101-2020_10_26_351783	208	56	funder	funder	NN
10_1101-2020_10_26_351783	208	57	.	.	.
10_1101-2020_10_26_351783	209	1	All	all	DT
10_1101-2020_10_26_351783	209	2	rights	right	NNS
10_1101-2020_10_26_351783	209	3	reserved	reserve	VBD
10_1101-2020_10_26_351783	209	4	.	.	.
10_1101-2020_10_26_351783	210	1	No	no	DT
10_1101-2020_10_26_351783	210	2	reuse	reuse	NN
10_1101-2020_10_26_351783	210	3	allowed	allow	VBN
10_1101-2020_10_26_351783	210	4	without	without	IN
10_1101-2020_10_26_351783	210	5	permission	permission	NN
10_1101-2020_10_26_351783	210	6	.	.	.
10_1101-2020_10_26_351783	211	1	The	the	DT
10_1101-2020_10_26_351783	211	2	copyright	copyright	NN
10_1101-2020_10_26_351783	211	3	holder	holder	NN
10_1101-2020_10_26_351783	211	4	for	for	IN
10_1101-2020_10_26_351783	211	5	this	this	DT
10_1101-2020_10_26_351783	211	6	preprintthis	preprintthis	NN
10_1101-2020_10_26_351783	211	7	version	version	NN
10_1101-2020_10_26_351783	211	8	posted	post	VBD
10_1101-2020_10_26_351783	211	9	January	January	NNP
10_1101-2020_10_26_351783	211	10	6	6	CD
10_1101-2020_10_26_351783	211	11	,	,	,
10_1101-2020_10_26_351783	211	12	2021	2021	CD
10_1101-2020_10_26_351783	211	13	.	.	.
10_1101-2020_10_26_351783	211	14	;	;	:
10_1101-2020_10_26_351783	211	15	https://doi.org/10.1101/2020.10.26.351783doi	https://doi.org/10.1101/2020.10.26.351783doi	ADD
10_1101-2020_10_26_351783	211	16	:	:	:
10_1101-2020_10_26_351783	211	17	bioRxiv	biorxiv	IN
10_1101-2020_10_26_351783	211	18	preprint	preprint	NN
10_1101-2020_10_26_351783	211	19	https://doi.org/10.1101/2020.10.26.351783	https://doi.org/10.1101/2020.10.26.351783	CC
10_1101-2020_10_26_351783	211	20	15	15	CD
10_1101-2020_10_26_351783	211	21	DISCUSSION	discussion	NN
10_1101-2020_10_26_351783	211	22	The	the	DT
10_1101-2020_10_26_351783	211	23	analysis	analysis	NN
10_1101-2020_10_26_351783	211	24	of	of	IN
10_1101-2020_10_26_351783	211	25	case	case	NN
10_1101-2020_10_26_351783	211	26	control	control	NNP
10_1101-2020_10_26_351783	211	27	data	datum	NNS
10_1101-2020_10_26_351783	211	28	in	in	IN
10_1101-2020_10_26_351783	211	29	the	the	DT
10_1101-2020_10_26_351783	211	30	context	context	NN
10_1101-2020_10_26_351783	211	31	of	of	IN
10_1101-2020_10_26_351783	211	32	networks	network	NNS
10_1101-2020_10_26_351783	211	33	has	have	VBZ
10_1101-2020_10_26_351783	211	34	gained	gain	VBN
10_1101-2020_10_26_351783	211	35	increased	increase	VBN
10_1101-2020_10_26_351783	211	36	interest	interest	NN
10_1101-2020_10_26_351783	211	37	to	to	TO
10_1101-2020_10_26_351783	211	38	detect	detect	VB
10_1101-2020_10_26_351783	211	39	consistent	consistent	JJ
10_1101-2020_10_26_351783	211	40	robust	robust	JJ
10_1101-2020_10_26_351783	211	41	gene	gene	NN
10_1101-2020_10_26_351783	211	42	signatures	signature	NNS
10_1101-2020_10_26_351783	211	43	of	of	IN
10_1101-2020_10_26_351783	211	44	individual	individual	JJ
10_1101-2020_10_26_351783	211	45	diseases	disease	NNS
10_1101-2020_10_26_351783	211	46	.	.	.
10_1101-2020_10_26_351783	212	1	The	the	DT
10_1101-2020_10_26_351783	212	2	application	application	NN
10_1101-2020_10_26_351783	212	3	of	of	IN
10_1101-2020_10_26_351783	212	4	disease	disease	NN
10_1101-2020_10_26_351783	212	5	modules	module	NNS
10_1101-2020_10_26_351783	212	6	might	may	MD
10_1101-2020_10_26_351783	212	7	vary	vary	VB
10_1101-2020_10_26_351783	212	8	for	for	IN
10_1101-2020_10_26_351783	212	9	different	different	JJ
10_1101-2020_10_26_351783	212	10	researchers	researcher	NNS
10_1101-2020_10_26_351783	212	11	,	,	,
10_1101-2020_10_26_351783	212	12	but	but	CC
10_1101-2020_10_26_351783	212	13	here	here	RB
10_1101-2020_10_26_351783	212	14	we	-PRON-	PRP
10_1101-2020_10_26_351783	212	15	systematically	systematically	RB
10_1101-2020_10_26_351783	212	16	aimed	aim	VBD
10_1101-2020_10_26_351783	212	17	at	at	IN
10_1101-2020_10_26_351783	212	18	the	the	DT
10_1101-2020_10_26_351783	212	19	detection	detection	NN
10_1101-2020_10_26_351783	212	20	of	of	IN
10_1101-2020_10_26_351783	212	21	disease	disease	NN
10_1101-2020_10_26_351783	212	22	genes	gene	NNS
10_1101-2020_10_26_351783	212	23	supported	support	VBN
10_1101-2020_10_26_351783	212	24	by	by	IN
10_1101-2020_10_26_351783	212	25	genetic	genetic	NNP
10_1101-2020_10_26_351783	212	26	association	association	NNP
10_1101-2020_10_26_351783	212	27	.	.	.
10_1101-2020_10_26_351783	213	1	For	for	IN
10_1101-2020_10_26_351783	213	2	this	this	DT
10_1101-2020_10_26_351783	213	3	purpose	purpose	NN
10_1101-2020_10_26_351783	213	4	,	,	,
10_1101-2020_10_26_351783	213	5	our	-PRON-	PRP$
10_1101-2020_10_26_351783	213	6	study	study	NN
10_1101-2020_10_26_351783	213	7	of	of	IN
10_1101-2020_10_26_351783	213	8	the	the	DT
10_1101-2020_10_26_351783	213	9	transcriptome	transcriptome	JJ
10_1101-2020_10_26_351783	213	10	and	and	CC
10_1101-2020_10_26_351783	213	11	methylome	methylome	JJ
10_1101-2020_10_26_351783	213	12	profiles	profile	NNS
10_1101-2020_10_26_351783	213	13	of	of	IN
10_1101-2020_10_26_351783	213	14	19	19	CD
10_1101-2020_10_26_351783	213	15	diseases	disease	NNS
10_1101-2020_10_26_351783	213	16	showed	show	VBD
10_1101-2020_10_26_351783	213	17	significant	significant	JJ
10_1101-2020_10_26_351783	213	18	GWAS	GWAS	NNP
10_1101-2020_10_26_351783	213	19	enrichments	enrichment	NNS
10_1101-2020_10_26_351783	213	20	for	for	IN
10_1101-2020_10_26_351783	213	21	several	several	JJ
10_1101-2020_10_26_351783	213	22	inflammatory	inflammatory	JJ
10_1101-2020_10_26_351783	213	23	and	and	CC
10_1101-2020_10_26_351783	213	24	heart	heart	NN
10_1101-2020_10_26_351783	213	25	diseases	disease	NNS
10_1101-2020_10_26_351783	213	26	,	,	,
10_1101-2020_10_26_351783	213	27	while	while	IN
10_1101-2020_10_26_351783	213	28	psychiatric	psychiatric	JJ
10_1101-2020_10_26_351783	213	29	disorders	disorder	NNS
10_1101-2020_10_26_351783	213	30	showed	show	VBD
10_1101-2020_10_26_351783	213	31	no	no	DT
10_1101-2020_10_26_351783	213	32	enrichments	enrichment	NNS
10_1101-2020_10_26_351783	213	33	and	and	CC
10_1101-2020_10_26_351783	213	34	might	may	MD
10_1101-2020_10_26_351783	213	35	not	not	RB
10_1101-2020_10_26_351783	213	36	be	be	VB
10_1101-2020_10_26_351783	213	37	suitable	suitable	JJ
10_1101-2020_10_26_351783	213	38	for	for	IN
10_1101-2020_10_26_351783	213	39	GWAS	GWAS	NNP
10_1101-2020_10_26_351783	213	40	validation	validation	NN
10_1101-2020_10_26_351783	213	41	of	of	IN
10_1101-2020_10_26_351783	213	42	modules	module	NNS
10_1101-2020_10_26_351783	213	43	,	,	,
10_1101-2020_10_26_351783	213	44	potentially	potentially	RB
10_1101-2020_10_26_351783	213	45	due	due	JJ
10_1101-2020_10_26_351783	213	46	to	to	IN
10_1101-2020_10_26_351783	213	47	differences	difference	NNS
10_1101-2020_10_26_351783	213	48	in	in	IN
10_1101-2020_10_26_351783	213	49	affected	affected	JJ
10_1101-2020_10_26_351783	213	50	tissue	tissue	NN
10_1101-2020_10_26_351783	213	51	types	type	NNS
10_1101-2020_10_26_351783	213	52	and	and	CC
10_1101-2020_10_26_351783	213	53	sampling	sampling	NN
10_1101-2020_10_26_351783	213	54	points	point	NNS
10_1101-2020_10_26_351783	213	55	.	.	.
10_1101-2020_10_26_351783	214	1	However	however	RB
10_1101-2020_10_26_351783	214	2	,	,	,
10_1101-2020_10_26_351783	214	3	analysis	analysis	NN
10_1101-2020_10_26_351783	214	4	of	of	IN
10_1101-2020_10_26_351783	214	5	the	the	DT
10_1101-2020_10_26_351783	214	6	significant	significant	JJ
10_1101-2020_10_26_351783	214	7	results	result	NNS
10_1101-2020_10_26_351783	214	8	showed	show	VBD
10_1101-2020_10_26_351783	214	9	that	that	IN
10_1101-2020_10_26_351783	214	10	methods	method	NNS
10_1101-2020_10_26_351783	214	11	based	base	VBN
10_1101-2020_10_26_351783	214	12	of	of	IN
10_1101-2020_10_26_351783	214	13	differentially	differentially	RB
10_1101-2020_10_26_351783	214	14	expressed	express	VBN
10_1101-2020_10_26_351783	214	15	cliques	clique	NNS
10_1101-2020_10_26_351783	214	16	in	in	IN
10_1101-2020_10_26_351783	214	17	the	the	DT
10_1101-2020_10_26_351783	214	18	protein	protein	NN
10_1101-2020_10_26_351783	214	19	-	-	HYPH
10_1101-2020_10_26_351783	214	20	protein	protein	NN
10_1101-2020_10_26_351783	214	21	interaction	interaction	NN
10_1101-2020_10_26_351783	214	22	network	network	NN
10_1101-2020_10_26_351783	214	23	demonstrated	demonstrate	VBD
10_1101-2020_10_26_351783	214	24	the	the	DT
10_1101-2020_10_26_351783	214	25	strongest	strong	JJS
10_1101-2020_10_26_351783	214	26	enrichments	enrichment	NNS
10_1101-2020_10_26_351783	214	27	(	(	-LRB-
10_1101-2020_10_26_351783	214	28	highest	high	JJS
10_1101-2020_10_26_351783	214	29	scoring	score	VBG
10_1101-2020_10_26_351783	214	30	for	for	IN
10_1101-2020_10_26_351783	214	31	Clique	Clique	NNP
10_1101-2020_10_26_351783	214	32	SuM	sum	NN
10_1101-2020_10_26_351783	214	33	)	)	-RRB-
10_1101-2020_10_26_351783	214	34	,	,	,
10_1101-2020_10_26_351783	214	35	while	while	IN
10_1101-2020_10_26_351783	214	36	those	those	DT
10_1101-2020_10_26_351783	214	37	based	base	VBN
10_1101-2020_10_26_351783	214	38	primarily	primarily	RB
10_1101-2020_10_26_351783	214	39	on	on	IN
10_1101-2020_10_26_351783	214	40	correlations	correlation	NNS
10_1101-2020_10_26_351783	214	41	,	,	,
10_1101-2020_10_26_351783	214	42	like	like	IN
10_1101-2020_10_26_351783	214	43	WGCNA	WGCNA	NNP
10_1101-2020_10_26_351783	214	44	,	,	,
10_1101-2020_10_26_351783	214	45	showed	show	VBD
10_1101-2020_10_26_351783	214	46	weak	weak	JJ
10_1101-2020_10_26_351783	214	47	enrichments	enrichment	NNS
10_1101-2020_10_26_351783	214	48	.	.	.
10_1101-2020_10_26_351783	215	1	A	a	DT
10_1101-2020_10_26_351783	215	2	potential	potential	JJ
10_1101-2020_10_26_351783	215	3	reason	reason	NN
10_1101-2020_10_26_351783	215	4	for	for	IN
10_1101-2020_10_26_351783	215	5	this	this	DT
10_1101-2020_10_26_351783	215	6	could	could	MD
10_1101-2020_10_26_351783	215	7	be	be	VB
10_1101-2020_10_26_351783	215	8	that	that	IN
10_1101-2020_10_26_351783	215	9	GWAS	GWAS	NNP
10_1101-2020_10_26_351783	215	10	has	have	VBZ
10_1101-2020_10_26_351783	215	11	shown	show	VBN
10_1101-2020_10_26_351783	215	12	to	to	TO
10_1101-2020_10_26_351783	215	13	be	be	VB
10_1101-2020_10_26_351783	215	14	mostly	mostly	RB
10_1101-2020_10_26_351783	215	15	associated	associate	VBN
10_1101-2020_10_26_351783	215	16	to	to	IN
10_1101-2020_10_26_351783	215	17	the	the	DT
10_1101-2020_10_26_351783	215	18	central	central	JJ
10_1101-2020_10_26_351783	215	19	genes	gene	NNS
10_1101-2020_10_26_351783	215	20	of	of	IN
10_1101-2020_10_26_351783	215	21	the	the	DT
10_1101-2020_10_26_351783	215	22	protein	protein	NN
10_1101-2020_10_26_351783	215	23	-	-	HYPH
10_1101-2020_10_26_351783	215	24	protein	protein	NN
10_1101-2020_10_26_351783	215	25	interaction	interaction	NN
10_1101-2020_10_26_351783	215	26	(	(	-LRB-
10_1101-2020_10_26_351783	215	27	PPI	PPI	NNP
10_1101-2020_10_26_351783	215	28	)	)	-RRB-
10_1101-2020_10_26_351783	215	29	network	network	NN
10_1101-2020_10_26_351783	215	30	,	,	,
10_1101-2020_10_26_351783	215	31	but	but	CC
10_1101-2020_10_26_351783	215	32	our	-PRON-	PRP$
10_1101-2020_10_26_351783	215	33	analysis	analysis	NN
10_1101-2020_10_26_351783	215	34	demonstrated	demonstrate	VBD
10_1101-2020_10_26_351783	215	35	that	that	IN
10_1101-2020_10_26_351783	215	36	the	the	DT
10_1101-2020_10_26_351783	215	37	correlation	correlation	NN
10_1101-2020_10_26_351783	215	38	between	between	IN
10_1101-2020_10_26_351783	215	39	GWAS	GWAS	NNP
10_1101-2020_10_26_351783	215	40	enrichment	enrichment	NN
10_1101-2020_10_26_351783	215	41	and	and	CC
10_1101-2020_10_26_351783	215	42	centrality	centrality	NN
10_1101-2020_10_26_351783	215	43	was	be	VBD
10_1101-2020_10_26_351783	215	44	non	non	JJ
10_1101-2020_10_26_351783	215	45	-	-	JJ
10_1101-2020_10_26_351783	215	46	significant	significant	JJ
10_1101-2020_10_26_351783	215	47	.	.	.
10_1101-2020_10_26_351783	216	1	We	-PRON-	PRP
10_1101-2020_10_26_351783	216	2	also	also	RB
10_1101-2020_10_26_351783	216	3	tested	test	VBD
10_1101-2020_10_26_351783	216	4	whether	whether	IN
10_1101-2020_10_26_351783	216	5	there	there	EX
10_1101-2020_10_26_351783	216	6	was	be	VBD
10_1101-2020_10_26_351783	216	7	an	an	DT
10_1101-2020_10_26_351783	216	8	improvement	improvement	NN
10_1101-2020_10_26_351783	216	9	using	use	VBG
10_1101-2020_10_26_351783	216	10	consensus	consensus	NN
10_1101-2020_10_26_351783	216	11	approaches	approach	NNS
10_1101-2020_10_26_351783	216	12	that	that	WDT
10_1101-2020_10_26_351783	216	13	counted	count	VBD
10_1101-2020_10_26_351783	216	14	the	the	DT
10_1101-2020_10_26_351783	216	15	frequency	frequency	NN
10_1101-2020_10_26_351783	216	16	of	of	IN
10_1101-2020_10_26_351783	216	17	the	the	DT
10_1101-2020_10_26_351783	216	18	result	result	NN
10_1101-2020_10_26_351783	216	19	of	of	IN
10_1101-2020_10_26_351783	216	20	multiple	multiple	JJ
10_1101-2020_10_26_351783	216	21	methods	method	NNS
10_1101-2020_10_26_351783	216	22	but	but	CC
10_1101-2020_10_26_351783	216	23	found	find	VBD
10_1101-2020_10_26_351783	216	24	this	this	DT
10_1101-2020_10_26_351783	216	25	not	not	RB
10_1101-2020_10_26_351783	216	26	to	to	TO
10_1101-2020_10_26_351783	216	27	increase	increase	VB
10_1101-2020_10_26_351783	216	28	performance	performance	NN
10_1101-2020_10_26_351783	216	29	.	.	.
10_1101-2020_10_26_351783	217	1	Moreover	moreover	RB
10_1101-2020_10_26_351783	217	2	,	,	,
10_1101-2020_10_26_351783	217	3	we	-PRON-	PRP
10_1101-2020_10_26_351783	217	4	tested	test	VBD
10_1101-2020_10_26_351783	217	5	the	the	DT
10_1101-2020_10_26_351783	217	6	same	same	JJ
10_1101-2020_10_26_351783	217	7	strategy	strategy	NN
10_1101-2020_10_26_351783	217	8	on	on	IN
10_1101-2020_10_26_351783	217	9	a	a	DT
10_1101-2020_10_26_351783	217	10	set	set	NN
10_1101-2020_10_26_351783	217	11	of	of	IN
10_1101-2020_10_26_351783	217	12	inflammatory	inflammatory	JJ
10_1101-2020_10_26_351783	217	13	,	,	,
10_1101-2020_10_26_351783	217	14	glycemic	glycemic	JJ
10_1101-2020_10_26_351783	217	15	,	,	,
10_1101-2020_10_26_351783	217	16	and	and	CC
10_1101-2020_10_26_351783	217	17	autoimmune	autoimmune	JJ
10_1101-2020_10_26_351783	217	18	methylation	methylation	NN
10_1101-2020_10_26_351783	217	19	datasets	dataset	NNS
10_1101-2020_10_26_351783	217	20	and	and	CC
10_1101-2020_10_26_351783	217	21	found	find	VBD
10_1101-2020_10_26_351783	217	22	similar	similar	JJ
10_1101-2020_10_26_351783	217	23	results	result	NNS
10_1101-2020_10_26_351783	217	24	.	.	.
10_1101-2020_10_26_351783	218	1	We	-PRON-	PRP
10_1101-2020_10_26_351783	218	2	would	would	MD
10_1101-2020_10_26_351783	218	3	like	like	VB
10_1101-2020_10_26_351783	218	4	to	to	TO
10_1101-2020_10_26_351783	218	5	emphasize	emphasize	VB
10_1101-2020_10_26_351783	218	6	that	that	DT
10_1101-2020_10_26_351783	218	7	,	,	,
10_1101-2020_10_26_351783	218	8	rather	rather	RB
10_1101-2020_10_26_351783	218	9	than	than	IN
10_1101-2020_10_26_351783	218	10	scoring	score	VBG
10_1101-2020_10_26_351783	218	11	a	a	DT
10_1101-2020_10_26_351783	218	12	single	single	JJ
10_1101-2020_10_26_351783	218	13	best	good	JJS
10_1101-2020_10_26_351783	218	14	working	working	NN
10_1101-2020_10_26_351783	218	15	method	method	NN
10_1101-2020_10_26_351783	218	16	,	,	,
10_1101-2020_10_26_351783	218	17	our	-PRON-	PRP$
10_1101-2020_10_26_351783	218	18	result	result	NN
10_1101-2020_10_26_351783	218	19	is	be	VBZ
10_1101-2020_10_26_351783	218	20	a	a	DT
10_1101-2020_10_26_351783	218	21	pipeline	pipeline	NN
10_1101-2020_10_26_351783	218	22	for	for	IN
10_1101-2020_10_26_351783	218	23	evaluating	evaluate	VBG
10_1101-2020_10_26_351783	218	24	modules	module	NNS
10_1101-2020_10_26_351783	218	25	using	use	VBG
10_1101-2020_10_26_351783	218	26	independent	independent	JJ
10_1101-2020_10_26_351783	218	27	high	high	JJ
10_1101-2020_10_26_351783	218	28	-	-	HYPH
10_1101-2020_10_26_351783	218	29	throughput	throughput	NN
10_1101-2020_10_26_351783	218	30	enrichments	enrichment	NNS
10_1101-2020_10_26_351783	218	31	.	.	.
10_1101-2020_10_26_351783	219	1	The	the	DT
10_1101-2020_10_26_351783	219	2	work	work	NN
10_1101-2020_10_26_351783	219	3	on	on	IN
10_1101-2020_10_26_351783	219	4	transcription	transcription	NN
10_1101-2020_10_26_351783	219	5	and	and	CC
10_1101-2020_10_26_351783	219	6	methylation	methylation	NN
10_1101-2020_10_26_351783	219	7	datasets	dataset	NNS
10_1101-2020_10_26_351783	219	8	suggested	suggest	VBD
10_1101-2020_10_26_351783	219	9	that	that	IN
10_1101-2020_10_26_351783	219	10	MS	MS	NNP
10_1101-2020_10_26_351783	219	11	is	be	VBZ
10_1101-2020_10_26_351783	219	12	a	a	DT
10_1101-2020_10_26_351783	219	13	disease	disease	NN
10_1101-2020_10_26_351783	219	14	highly	highly	RB
10_1101-2020_10_26_351783	219	15	enriched	enrich	VBN
10_1101-2020_10_26_351783	219	16	for	for	IN
10_1101-2020_10_26_351783	219	17	GWAS	GWAS	NNP
10_1101-2020_10_26_351783	219	18	,	,	,
10_1101-2020_10_26_351783	219	19	and	and	CC
10_1101-2020_10_26_351783	219	20	we	-PRON-	PRP
10_1101-2020_10_26_351783	219	21	therefore	therefore	RB
10_1101-2020_10_26_351783	219	22	tested	test	VBD
10_1101-2020_10_26_351783	219	23	if	if	IN
10_1101-2020_10_26_351783	219	24	increased	increase	VBN
10_1101-2020_10_26_351783	219	25	enrichments	enrichment	NNS
10_1101-2020_10_26_351783	219	26	could	could	MD
10_1101-2020_10_26_351783	219	27	be	be	VB
10_1101-2020_10_26_351783	219	28	derived	derive	VBN
10_1101-2020_10_26_351783	219	29	by	by	IN
10_1101-2020_10_26_351783	219	30	their	-PRON-	PRP$
10_1101-2020_10_26_351783	219	31	integration	integration	NN
10_1101-2020_10_26_351783	219	32	.	.	.
10_1101-2020_10_26_351783	220	1	We	-PRON-	PRP
10_1101-2020_10_26_351783	220	2	found	find	VBD
10_1101-2020_10_26_351783	220	3	20	20	CD
10_1101-2020_10_26_351783	220	4	publicly	publicly	RB
10_1101-2020_10_26_351783	220	5	available	available	JJ
10_1101-2020_10_26_351783	220	6	datasets	dataset	NNS
10_1101-2020_10_26_351783	220	7	and	and	CC
10_1101-2020_10_26_351783	220	8	run	run	VB
10_1101-2020_10_26_351783	220	9	assessment	assessment	NN
10_1101-2020_10_26_351783	220	10	for	for	IN
10_1101-2020_10_26_351783	220	11	both	both	DT
10_1101-2020_10_26_351783	220	12	omics	omic	NNS
10_1101-2020_10_26_351783	220	13	independently	independently	RB
10_1101-2020_10_26_351783	220	14	,	,	,
10_1101-2020_10_26_351783	220	15	which	which	WDT
10_1101-2020_10_26_351783	220	16	again	again	RB
10_1101-2020_10_26_351783	220	17	showed	show	VBD
10_1101-2020_10_26_351783	220	18	Clique	Clique	NNP
10_1101-2020_10_26_351783	220	19	SuM	sum	NN
10_1101-2020_10_26_351783	220	20	to	to	TO
10_1101-2020_10_26_351783	220	21	score	score	VB
10_1101-2020_10_26_351783	220	22	highest	highest	RBS
10_1101-2020_10_26_351783	220	23	.	.	.
10_1101-2020_10_26_351783	221	1	We	-PRON-	PRP
10_1101-2020_10_26_351783	221	2	then	then	RB
10_1101-2020_10_26_351783	221	3	tested	test	VBD
10_1101-2020_10_26_351783	221	4	if	if	IN
10_1101-2020_10_26_351783	221	5	improved	improve	VBN
10_1101-2020_10_26_351783	221	6	results	result	NNS
10_1101-2020_10_26_351783	221	7	could	could	MD
10_1101-2020_10_26_351783	221	8	be	be	VB
10_1101-2020_10_26_351783	221	9	obtained	obtain	VBN
10_1101-2020_10_26_351783	221	10	using	use	VBG
10_1101-2020_10_26_351783	221	11	modules	module	NNS
10_1101-2020_10_26_351783	221	12	from	from	IN
10_1101-2020_10_26_351783	221	13	multiple	multiple	JJ
10_1101-2020_10_26_351783	221	14	datasets	dataset	NNS
10_1101-2020_10_26_351783	221	15	of	of	IN
10_1101-2020_10_26_351783	221	16	these	these	DT
10_1101-2020_10_26_351783	221	17	two	two	CD
10_1101-2020_10_26_351783	221	18	omics	omic	NNS
10_1101-2020_10_26_351783	221	19	using	use	VBG
10_1101-2020_10_26_351783	221	20	consensus	consensus	NN
10_1101-2020_10_26_351783	221	21	modules	module	NNS
10_1101-2020_10_26_351783	221	22	from	from	IN
10_1101-2020_10_26_351783	221	23	Clique	Clique	NNP
10_1101-2020_10_26_351783	221	24	SuM.	SuM.	NNP
10_1101-2020_10_26_351783	222	1	This	this	DT
10_1101-2020_10_26_351783	222	2	resulted	result	VBD
10_1101-2020_10_26_351783	222	3	in	in	IN
10_1101-2020_10_26_351783	222	4	a	a	DT
10_1101-2020_10_26_351783	222	5	module	module	NN
10_1101-2020_10_26_351783	222	6	of	of	IN
10_1101-2020_10_26_351783	222	7	220	220	CD
10_1101-2020_10_26_351783	222	8	genes	gene	NNS
10_1101-2020_10_26_351783	222	9	highly	highly	RB
10_1101-2020_10_26_351783	222	10	enriched	enrich	VBN
10_1101-2020_10_26_351783	222	11	for	for	IN
10_1101-2020_10_26_351783	222	12	GWAS	GWAS	NNP
10_1101-2020_10_26_351783	222	13	(	(	-LRB-
10_1101-2020_10_26_351783	222	14	P	p	NN
10_1101-2020_10_26_351783	222	15	=	=	SYM
10_1101-2020_10_26_351783	222	16	8.8	8.8	CD
10_1101-2020_10_26_351783	222	17	x	x	SYM
10_1101-2020_10_26_351783	222	18	10	10	CD
10_1101-2020_10_26_351783	222	19	-	-	SYM
10_1101-2020_10_26_351783	222	20	9	9	CD
10_1101-2020_10_26_351783	222	21	)	)	-RRB-
10_1101-2020_10_26_351783	222	22	.	.	.
10_1101-2020_10_26_351783	223	1	The	the	DT
10_1101-2020_10_26_351783	223	2	multi-	multi-	JJ
10_1101-2020_10_26_351783	223	3	omic	omic	JJ
10_1101-2020_10_26_351783	223	4	module	module	NN
10_1101-2020_10_26_351783	223	5	was	be	VBD
10_1101-2020_10_26_351783	223	6	highly	highly	RB
10_1101-2020_10_26_351783	223	7	enriched	enrich	VBN
10_1101-2020_10_26_351783	223	8	in	in	IN
10_1101-2020_10_26_351783	223	9	immune	immune	NN
10_1101-2020_10_26_351783	223	10	-	-	HYPH
10_1101-2020_10_26_351783	223	11	associated	associate	VBN
10_1101-2020_10_26_351783	223	12	pathways	pathway	NNS
10_1101-2020_10_26_351783	223	13	,	,	,
10_1101-2020_10_26_351783	223	14	such	such	JJ
10_1101-2020_10_26_351783	223	15	as	as	IN
10_1101-2020_10_26_351783	223	16	T	T	NNP
10_1101-2020_10_26_351783	223	17	cell	cell	NN
10_1101-2020_10_26_351783	223	18	and	and	CC
10_1101-2020_10_26_351783	223	19	B	b	NN
10_1101-2020_10_26_351783	223	20	cell	cell	NN
10_1101-2020_10_26_351783	223	21	receptor	receptor	NN
10_1101-2020_10_26_351783	223	22	(	(	-LRB-
10_1101-2020_10_26_351783	223	23	which	which	WDT
10_1101-2020_10_26_351783	223	24	was	be	VBD
10_1101-2020_10_26_351783	223	25	not	not	RB
10_1101-2020_10_26_351783	223	26	certified	certify	VBN
10_1101-2020_10_26_351783	223	27	by	by	IN
10_1101-2020_10_26_351783	223	28	peer	peer	NN
10_1101-2020_10_26_351783	223	29	review	review	NN
10_1101-2020_10_26_351783	223	30	)	)	-RRB-
10_1101-2020_10_26_351783	223	31	is	be	VBZ
10_1101-2020_10_26_351783	223	32	the	the	DT
10_1101-2020_10_26_351783	223	33	author	author	NN
10_1101-2020_10_26_351783	223	34	/	/	SYM
10_1101-2020_10_26_351783	223	35	funder	funder	NN
10_1101-2020_10_26_351783	223	36	.	.	.
10_1101-2020_10_26_351783	224	1	All	all	DT
10_1101-2020_10_26_351783	224	2	rights	right	NNS
10_1101-2020_10_26_351783	224	3	reserved	reserve	VBD
10_1101-2020_10_26_351783	224	4	.	.	.
10_1101-2020_10_26_351783	225	1	No	no	DT
10_1101-2020_10_26_351783	225	2	reuse	reuse	NN
10_1101-2020_10_26_351783	225	3	allowed	allow	VBN
10_1101-2020_10_26_351783	225	4	without	without	IN
10_1101-2020_10_26_351783	225	5	permission	permission	NN
10_1101-2020_10_26_351783	225	6	.	.	.
10_1101-2020_10_26_351783	226	1	The	the	DT
10_1101-2020_10_26_351783	226	2	copyright	copyright	NN
10_1101-2020_10_26_351783	226	3	holder	holder	NN
10_1101-2020_10_26_351783	226	4	for	for	IN
10_1101-2020_10_26_351783	226	5	this	this	DT
10_1101-2020_10_26_351783	226	6	preprintthis	preprintthis	NN
10_1101-2020_10_26_351783	226	7	version	version	NN
10_1101-2020_10_26_351783	226	8	posted	post	VBD
10_1101-2020_10_26_351783	226	9	January	January	NNP
10_1101-2020_10_26_351783	226	10	6	6	CD
10_1101-2020_10_26_351783	226	11	,	,	,
10_1101-2020_10_26_351783	226	12	2021	2021	CD
10_1101-2020_10_26_351783	226	13	.	.	.
10_1101-2020_10_26_351783	226	14	;	;	:
10_1101-2020_10_26_351783	226	15	https://doi.org/10.1101/2020.10.26.351783doi	https://doi.org/10.1101/2020.10.26.351783doi	ADD
10_1101-2020_10_26_351783	226	16	:	:	:
10_1101-2020_10_26_351783	226	17	bioRxiv	biorxiv	VB
10_1101-2020_10_26_351783	226	18	preprint	preprint	NN
10_1101-2020_10_26_351783	226	19	https://doi.org/10.1101/2020.10.26.351783	https://doi.org/10.1101/2020.10.26.351783	NNP
10_1101-2020_10_26_351783	226	20	16	16	CD
10_1101-2020_10_26_351783	226	21	signaling	signal	VBG
10_1101-2020_10_26_351783	226	22	,	,	,
10_1101-2020_10_26_351783	226	23	Th1	th1	NN
10_1101-2020_10_26_351783	226	24	/	/	SYM
10_1101-2020_10_26_351783	226	25	Th2	th2	NN
10_1101-2020_10_26_351783	226	26	differentiation	differentiation	NN
10_1101-2020_10_26_351783	226	27	,	,	,
10_1101-2020_10_26_351783	226	28	or	or	CC
10_1101-2020_10_26_351783	226	29	leukocyte	leukocyte	NN
10_1101-2020_10_26_351783	226	30	transendothelial	transendothelial	NN
10_1101-2020_10_26_351783	226	31	migration	migration	NN
10_1101-2020_10_26_351783	226	32	.	.	.
10_1101-2020_10_26_351783	227	1	These	these	DT
10_1101-2020_10_26_351783	227	2	results	result	NNS
10_1101-2020_10_26_351783	227	3	conform	conform	VBP
10_1101-2020_10_26_351783	227	4	with	with	IN
10_1101-2020_10_26_351783	227	5	the	the	DT
10_1101-2020_10_26_351783	227	6	current	current	JJ
10_1101-2020_10_26_351783	227	7	hypothesis	hypothesis	NN
10_1101-2020_10_26_351783	227	8	that	that	WDT
10_1101-2020_10_26_351783	227	9	MS	MS	NNP
10_1101-2020_10_26_351783	227	10	is	be	VBZ
10_1101-2020_10_26_351783	227	11	mediated	mediate	VBN
10_1101-2020_10_26_351783	227	12	by	by	IN
10_1101-2020_10_26_351783	227	13	an	an	DT
10_1101-2020_10_26_351783	227	14	autoreactive	autoreactive	JJ
10_1101-2020_10_26_351783	227	15	response	response	NN
10_1101-2020_10_26_351783	227	16	of	of	IN
10_1101-2020_10_26_351783	227	17	CD4	CD4	NNP
10_1101-2020_10_26_351783	227	18	+	+	SYM
10_1101-2020_10_26_351783	227	19	T	t	NN
10_1101-2020_10_26_351783	227	20	cells	cell	NNS
10_1101-2020_10_26_351783	227	21	against	against	IN
10_1101-2020_10_26_351783	227	22	myelin	myelin	NNP
10_1101-2020_10_26_351783	227	23	surrounding	surround	VBG
10_1101-2020_10_26_351783	227	24	neuronal	neuronal	JJ
10_1101-2020_10_26_351783	227	25	axons	axon	NNS
10_1101-2020_10_26_351783	227	26	,	,	,
10_1101-2020_10_26_351783	227	27	preceded	precede	VBN
10_1101-2020_10_26_351783	227	28	by	by	IN
10_1101-2020_10_26_351783	227	29	their	-PRON-	PRP$
10_1101-2020_10_26_351783	227	30	migration	migration	NN
10_1101-2020_10_26_351783	227	31	across	across	IN
10_1101-2020_10_26_351783	227	32	the	the	DT
10_1101-2020_10_26_351783	227	33	blood	blood	NN
10_1101-2020_10_26_351783	227	34	-	-	HYPH
10_1101-2020_10_26_351783	227	35	brain	brain	NN
10_1101-2020_10_26_351783	227	36	barrier	barrier	NN
10_1101-2020_10_26_351783	227	37	(	(	-LRB-
10_1101-2020_10_26_351783	227	38	BBB)[37	BBB)[37	NNP
10_1101-2020_10_26_351783	227	39	]	]	-RRB-
10_1101-2020_10_26_351783	227	40	.	.	.
10_1101-2020_10_26_351783	228	1	This	this	DT
10_1101-2020_10_26_351783	228	2	autoproliferation	autoproliferation	NN
10_1101-2020_10_26_351783	228	3	of	of	IN
10_1101-2020_10_26_351783	228	4	brain	brain	NN
10_1101-2020_10_26_351783	228	5	-	-	HYPH
10_1101-2020_10_26_351783	228	6	targeting	target	VBG
10_1101-2020_10_26_351783	228	7	Th1	th1	NN
10_1101-2020_10_26_351783	228	8	cells	cell	NNS
10_1101-2020_10_26_351783	228	9	has	have	VBZ
10_1101-2020_10_26_351783	228	10	been	be	VBN
10_1101-2020_10_26_351783	228	11	shown	show	VBN
10_1101-2020_10_26_351783	228	12	to	to	TO
10_1101-2020_10_26_351783	228	13	be	be	VB
10_1101-2020_10_26_351783	228	14	driven	drive	VBN
10_1101-2020_10_26_351783	228	15	by	by	IN
10_1101-2020_10_26_351783	228	16	memory	memory	NN
10_1101-2020_10_26_351783	228	17	B	b	NN
10_1101-2020_10_26_351783	228	18	cells	cell	NNS
10_1101-2020_10_26_351783	228	19	,	,	,
10_1101-2020_10_26_351783	228	20	in	in	IN
10_1101-2020_10_26_351783	228	21	a	a	DT
10_1101-2020_10_26_351783	228	22	process	process	NN
10_1101-2020_10_26_351783	228	23	mediated	mediate	VBN
10_1101-2020_10_26_351783	228	24	by	by	IN
10_1101-2020_10_26_351783	228	25	HLA	HLA	NNP
10_1101-2020_10_26_351783	228	26	-	-	HYPH
10_1101-2020_10_26_351783	228	27	DR15[38	DR15[38	NNP
10_1101-2020_10_26_351783	228	28	]	]	-RRB-
10_1101-2020_10_26_351783	228	29	.	.	.
10_1101-2020_10_26_351783	229	1	In	in	IN
10_1101-2020_10_26_351783	229	2	addition	addition	NN
10_1101-2020_10_26_351783	229	3	,	,	,
10_1101-2020_10_26_351783	229	4	another	another	DT
10_1101-2020_10_26_351783	229	5	enriched	enriched	JJ
10_1101-2020_10_26_351783	229	6	pathway	pathway	NN
10_1101-2020_10_26_351783	229	7	was	be	VBD
10_1101-2020_10_26_351783	229	8	VEGF	vegf	NN
10_1101-2020_10_26_351783	229	9	signaling	signal	VBG
10_1101-2020_10_26_351783	229	10	.	.	.
10_1101-2020_10_26_351783	230	1	MS	MS	NNP
10_1101-2020_10_26_351783	230	2	patients	patient	NNS
10_1101-2020_10_26_351783	230	3	present	present	VBP
10_1101-2020_10_26_351783	230	4	high	high	JJ
10_1101-2020_10_26_351783	230	5	serum	serum	NN
10_1101-2020_10_26_351783	230	6	VEGF	vegf	NN
10_1101-2020_10_26_351783	230	7	levels	level	NNS
10_1101-2020_10_26_351783	230	8	,	,	,
10_1101-2020_10_26_351783	230	9	which	which	WDT
10_1101-2020_10_26_351783	230	10	is	be	VBZ
10_1101-2020_10_26_351783	230	11	related	relate	VBN
10_1101-2020_10_26_351783	230	12	to	to	IN
10_1101-2020_10_26_351783	230	13	pro	pro	JJ
10_1101-2020_10_26_351783	230	14	-	-	JJ
10_1101-2020_10_26_351783	230	15	inflammatory	inflammatory	JJ
10_1101-2020_10_26_351783	230	16	functions	function	NNS
10_1101-2020_10_26_351783	230	17	and	and	CC
10_1101-2020_10_26_351783	230	18	can	can	MD
10_1101-2020_10_26_351783	230	19	alter	alter	VB
10_1101-2020_10_26_351783	230	20	the	the	DT
10_1101-2020_10_26_351783	230	21	permeability	permeability	NN
10_1101-2020_10_26_351783	230	22	of	of	IN
10_1101-2020_10_26_351783	230	23	the	the	DT
10_1101-2020_10_26_351783	230	24	BBB[39	BBB[39	NNP
10_1101-2020_10_26_351783	230	25	]	]	-RRB-
10_1101-2020_10_26_351783	230	26	.	.	.
10_1101-2020_10_26_351783	231	1	As	as	IN
10_1101-2020_10_26_351783	231	2	GWAS	GWAS	NNP
10_1101-2020_10_26_351783	231	3	was	be	VBD
10_1101-2020_10_26_351783	231	4	used	use	VBN
10_1101-2020_10_26_351783	231	5	for	for	IN
10_1101-2020_10_26_351783	231	6	method	method	NN
10_1101-2020_10_26_351783	231	7	prioritization	prioritization	NN
10_1101-2020_10_26_351783	231	8	we	-PRON-	PRP
10_1101-2020_10_26_351783	231	9	asked	ask	VBD
10_1101-2020_10_26_351783	231	10	if	if	IN
10_1101-2020_10_26_351783	231	11	modules	module	NNS
10_1101-2020_10_26_351783	231	12	instead	instead	RB
10_1101-2020_10_26_351783	231	13	could	could	MD
10_1101-2020_10_26_351783	231	14	be	be	VB
10_1101-2020_10_26_351783	231	15	validated	validate	VBN
10_1101-2020_10_26_351783	231	16	using	use	VBG
10_1101-2020_10_26_351783	231	17	epigenetics	epigenetic	NNS
10_1101-2020_10_26_351783	231	18	and	and	CC
10_1101-2020_10_26_351783	231	19	lifestyle	lifestyle	NN
10_1101-2020_10_26_351783	231	20	risk	risk	NN
10_1101-2020_10_26_351783	231	21	factor	factor	NN
10_1101-2020_10_26_351783	231	22	genes	gene	NNS
10_1101-2020_10_26_351783	231	23	that	that	WDT
10_1101-2020_10_26_351783	231	24	we	-PRON-	PRP
10_1101-2020_10_26_351783	231	25	identified	identify	VBD
10_1101-2020_10_26_351783	231	26	to	to	TO
10_1101-2020_10_26_351783	231	27	associate	associate	VB
10_1101-2020_10_26_351783	231	28	with	with	IN
10_1101-2020_10_26_351783	231	29	MS	MS	NNP
10_1101-2020_10_26_351783	231	30	.	.	.
10_1101-2020_10_26_351783	231	31	With	with	IN
10_1101-2020_10_26_351783	231	32	this	this	DT
10_1101-2020_10_26_351783	231	33	aim	aim	NN
10_1101-2020_10_26_351783	231	34	,	,	,
10_1101-2020_10_26_351783	231	35	we	-PRON-	PRP
10_1101-2020_10_26_351783	231	36	compiled	compile	VBD
10_1101-2020_10_26_351783	231	37	a	a	DT
10_1101-2020_10_26_351783	231	38	set	set	NN
10_1101-2020_10_26_351783	231	39	of	of	IN
10_1101-2020_10_26_351783	231	40	publicly	publicly	RB
10_1101-2020_10_26_351783	231	41	available	available	JJ
10_1101-2020_10_26_351783	231	42	data	datum	NNS
10_1101-2020_10_26_351783	231	43	from	from	IN
10_1101-2020_10_26_351783	231	44	omics	omic	NNS
10_1101-2020_10_26_351783	231	45	studies	study	NNS
10_1101-2020_10_26_351783	231	46	of	of	IN
10_1101-2020_10_26_351783	231	47	these	these	DT
10_1101-2020_10_26_351783	231	48	risk	risk	NN
10_1101-2020_10_26_351783	231	49	factors	factor	NNS
10_1101-2020_10_26_351783	231	50	in	in	IN
10_1101-2020_10_26_351783	231	51	healthy	healthy	JJ
10_1101-2020_10_26_351783	231	52	individuals	individual	NNS
10_1101-2020_10_26_351783	231	53	.	.	.
10_1101-2020_10_26_351783	232	1	This	this	DT
10_1101-2020_10_26_351783	232	2	analysis	analysis	NN
10_1101-2020_10_26_351783	232	3	demonstrated	demonstrate	VBD
10_1101-2020_10_26_351783	232	4	that	that	IN
10_1101-2020_10_26_351783	232	5	five	five	CD
10_1101-2020_10_26_351783	232	6	out	out	IN
10_1101-2020_10_26_351783	232	7	of	of	IN
10_1101-2020_10_26_351783	232	8	eight	eight	CD
10_1101-2020_10_26_351783	232	9	risk	risk	NN
10_1101-2020_10_26_351783	232	10	factors	factor	NNS
10_1101-2020_10_26_351783	232	11	were	be	VBD
10_1101-2020_10_26_351783	232	12	enriched	enrich	VBN
10_1101-2020_10_26_351783	232	13	in	in	IN
10_1101-2020_10_26_351783	232	14	our	-PRON-	PRP$
10_1101-2020_10_26_351783	232	15	module	module	NN
10_1101-2020_10_26_351783	232	16	.	.	.
10_1101-2020_10_26_351783	233	1	In	in	IN
10_1101-2020_10_26_351783	233	2	order	order	NN
10_1101-2020_10_26_351783	233	3	to	to	TO
10_1101-2020_10_26_351783	233	4	validate	validate	VB
10_1101-2020_10_26_351783	233	5	the	the	DT
10_1101-2020_10_26_351783	233	6	use	use	NN
10_1101-2020_10_26_351783	233	7	of	of	IN
10_1101-2020_10_26_351783	233	8	an	an	DT
10_1101-2020_10_26_351783	233	9	environmental	environmental	JJ
10_1101-2020_10_26_351783	233	10	assessment	assessment	NN
10_1101-2020_10_26_351783	233	11	using	use	VBG
10_1101-2020_10_26_351783	233	12	public	public	JJ
10_1101-2020_10_26_351783	233	13	domain	domain	NN
10_1101-2020_10_26_351783	233	14	risk	risk	NN
10_1101-2020_10_26_351783	233	15	factor	factor	NN
10_1101-2020_10_26_351783	233	16	association	association	NNP
10_1101-2020_10_26_351783	233	17	we	-PRON-	PRP
10_1101-2020_10_26_351783	233	18	found	find	VBD
10_1101-2020_10_26_351783	233	19	an	an	DT
10_1101-2020_10_26_351783	233	20	independent	independent	JJ
10_1101-2020_10_26_351783	233	21	methylome	methylome	JJ
10_1101-2020_10_26_351783	233	22	study	study	NN
10_1101-2020_10_26_351783	233	23	of	of	IN
10_1101-2020_10_26_351783	233	24	MS	MS	NNP
10_1101-2020_10_26_351783	233	25	comprising	comprise	VBG
10_1101-2020_10_26_351783	233	26	environmental	environmental	JJ
10_1101-2020_10_26_351783	233	27	data	datum	NNS
10_1101-2020_10_26_351783	233	28	for	for	IN
10_1101-2020_10_26_351783	233	29	each	each	DT
10_1101-2020_10_26_351783	233	30	MS	MS	NNP
10_1101-2020_10_26_351783	233	31	and	and	CC
10_1101-2020_10_26_351783	233	32	healthy	healthy	JJ
10_1101-2020_10_26_351783	233	33	individual	individual	NN
10_1101-2020_10_26_351783	233	34	.	.	.
10_1101-2020_10_26_351783	234	1	This	this	DT
10_1101-2020_10_26_351783	234	2	analysis	analysis	NN
10_1101-2020_10_26_351783	234	3	showed	show	VBD
10_1101-2020_10_26_351783	234	4	a	a	DT
10_1101-2020_10_26_351783	234	5	remarkable	remarkable	JJ
10_1101-2020_10_26_351783	234	6	enrichment	enrichment	NN
10_1101-2020_10_26_351783	234	7	of	of	IN
10_1101-2020_10_26_351783	234	8	the	the	DT
10_1101-2020_10_26_351783	234	9	220	220	CD
10_1101-2020_10_26_351783	234	10	module	module	JJ
10_1101-2020_10_26_351783	234	11	genes	gene	NNS
10_1101-2020_10_26_351783	234	12	by	by	IN
10_1101-2020_10_26_351783	234	13	217	217	CD
10_1101-2020_10_26_351783	234	14	to	to	TO
10_1101-2020_10_26_351783	234	15	differentially	differentially	RB
10_1101-2020_10_26_351783	234	16	methylated	methylate	VBN
10_1101-2020_10_26_351783	234	17	genes	gene	NNS
10_1101-2020_10_26_351783	234	18	for	for	IN
10_1101-2020_10_26_351783	234	19	MS	MS	NNP
10_1101-2020_10_26_351783	234	20	(	(	-LRB-
10_1101-2020_10_26_351783	234	21	P	p	NN
10_1101-2020_10_26_351783	234	22	=	=	SYM
10_1101-2020_10_26_351783	234	23	1.2	1.2	CD
10_1101-2020_10_26_351783	234	24	x	x	SYM
10_1101-2020_10_26_351783	234	25	10	10	CD
10_1101-2020_10_26_351783	234	26	-	-	SYM
10_1101-2020_10_26_351783	234	27	47	47	CD
10_1101-2020_10_26_351783	234	28	)	)	-RRB-
10_1101-2020_10_26_351783	234	29	,	,	,
10_1101-2020_10_26_351783	234	30	and	and	CC
10_1101-2020_10_26_351783	234	31	a	a	DT
10_1101-2020_10_26_351783	234	32	majority	majority	NN
10_1101-2020_10_26_351783	234	33	to	to	TO
10_1101-2020_10_26_351783	234	34	be	be	VB
10_1101-2020_10_26_351783	234	35	associated	associate	VBN
10_1101-2020_10_26_351783	234	36	with	with	IN
10_1101-2020_10_26_351783	234	37	the	the	DT
10_1101-2020_10_26_351783	234	38	tested	test	VBN
10_1101-2020_10_26_351783	234	39	risk	risk	NN
10_1101-2020_10_26_351783	234	40	factors	factor	NNS
10_1101-2020_10_26_351783	234	41	.	.	.
10_1101-2020_10_26_351783	235	1	In	in	IN
10_1101-2020_10_26_351783	235	2	contrast	contrast	NN
10_1101-2020_10_26_351783	235	3	to	to	TO
10_1101-2020_10_26_351783	235	4	previously	previously	RB
10_1101-2020_10_26_351783	235	5	known	know	VBN
10_1101-2020_10_26_351783	235	6	community	community	NN
10_1101-2020_10_26_351783	235	7	challenges	challenge	NNS
10_1101-2020_10_26_351783	235	8	,	,	,
10_1101-2020_10_26_351783	235	9	in	in	IN
10_1101-2020_10_26_351783	235	10	our	-PRON-	PRP$
10_1101-2020_10_26_351783	235	11	study	study	NN
10_1101-2020_10_26_351783	235	12	we	-PRON-	PRP
10_1101-2020_10_26_351783	235	13	not	not	RB
10_1101-2020_10_26_351783	235	14	only	only	RB
10_1101-2020_10_26_351783	235	15	used	use	VBD
10_1101-2020_10_26_351783	235	16	the	the	DT
10_1101-2020_10_26_351783	235	17	topological	topological	JJ
10_1101-2020_10_26_351783	235	18	property	property	NN
10_1101-2020_10_26_351783	235	19	of	of	IN
10_1101-2020_10_26_351783	235	20	the	the	DT
10_1101-2020_10_26_351783	235	21	network	network	NN
10_1101-2020_10_26_351783	235	22	,	,	,
10_1101-2020_10_26_351783	235	23	but	but	CC
10_1101-2020_10_26_351783	235	24	we	-PRON-	PRP
10_1101-2020_10_26_351783	235	25	also	also	RB
10_1101-2020_10_26_351783	235	26	combined	combine	VBD
10_1101-2020_10_26_351783	235	27	the	the	DT
10_1101-2020_10_26_351783	235	28	methods	method	NNS
10_1101-2020_10_26_351783	235	29	to	to	TO
10_1101-2020_10_26_351783	235	30	use	use	VB
10_1101-2020_10_26_351783	235	31	an	an	DT
10_1101-2020_10_26_351783	235	32	omics	omic	NNS
10_1101-2020_10_26_351783	235	33	-	-	HYPH
10_1101-2020_10_26_351783	235	34	based	base	VBN
10_1101-2020_10_26_351783	235	35	input	input	NN
10_1101-2020_10_26_351783	235	36	to	to	TO
10_1101-2020_10_26_351783	235	37	uncover	uncover	VB
10_1101-2020_10_26_351783	235	38	the	the	DT
10_1101-2020_10_26_351783	235	39	disease	disease	NN
10_1101-2020_10_26_351783	235	40	modules	module	NNS
10_1101-2020_10_26_351783	235	41	that	that	WDT
10_1101-2020_10_26_351783	235	42	might	may	MD
10_1101-2020_10_26_351783	235	43	be	be	VB
10_1101-2020_10_26_351783	235	44	dysregulated	dysregulate	VBN
10_1101-2020_10_26_351783	235	45	at	at	IN
10_1101-2020_10_26_351783	235	46	each	each	DT
10_1101-2020_10_26_351783	235	47	omics	omic	NNS
10_1101-2020_10_26_351783	235	48	level	level	NN
10_1101-2020_10_26_351783	235	49	,	,	,
10_1101-2020_10_26_351783	235	50	contributing	contribute	VBG
10_1101-2020_10_26_351783	235	51	to	to	IN
10_1101-2020_10_26_351783	235	52	the	the	DT
10_1101-2020_10_26_351783	235	53	diverse	diverse	JJ
10_1101-2020_10_26_351783	235	54	causative	causative	JJ
10_1101-2020_10_26_351783	235	55	mechanisms	mechanism	NNS
10_1101-2020_10_26_351783	235	56	behind	behind	IN
10_1101-2020_10_26_351783	235	57	complex	complex	JJ
10_1101-2020_10_26_351783	235	58	diseases	disease	NNS
10_1101-2020_10_26_351783	235	59	.	.	.
10_1101-2020_10_26_351783	236	1	Although	although	IN
10_1101-2020_10_26_351783	236	2	using	use	VBG
10_1101-2020_10_26_351783	236	3	the	the	DT
10_1101-2020_10_26_351783	236	4	PPI	PPI	NNP
10_1101-2020_10_26_351783	236	5	network	network	NN
10_1101-2020_10_26_351783	236	6	as	as	IN
10_1101-2020_10_26_351783	236	7	background	background	NN
10_1101-2020_10_26_351783	236	8	may	may	MD
10_1101-2020_10_26_351783	236	9	lead	lead	VB
10_1101-2020_10_26_351783	236	10	to	to	IN
10_1101-2020_10_26_351783	236	11	certain	certain	JJ
10_1101-2020_10_26_351783	236	12	knowledge	knowledge	NN
10_1101-2020_10_26_351783	236	13	bias	bias	NN
10_1101-2020_10_26_351783	236	14	,	,	,
10_1101-2020_10_26_351783	236	15	this	this	DT
10_1101-2020_10_26_351783	236	16	kind	kind	NN
10_1101-2020_10_26_351783	236	17	of	of	IN
10_1101-2020_10_26_351783	236	18	benchmark	benchmark	NN
10_1101-2020_10_26_351783	236	19	allowed	allow	VBD
10_1101-2020_10_26_351783	236	20	us	-PRON-	PRP
10_1101-2020_10_26_351783	236	21	to	to	TO
10_1101-2020_10_26_351783	236	22	look	look	VB
10_1101-2020_10_26_351783	236	23	at	at	IN
10_1101-2020_10_26_351783	236	24	the	the	DT
10_1101-2020_10_26_351783	236	25	relevant	relevant	JJ
10_1101-2020_10_26_351783	236	26	risk	risk	NN
10_1101-2020_10_26_351783	236	27	factors	factor	NNS
10_1101-2020_10_26_351783	236	28	.	.	.
10_1101-2020_10_26_351783	237	1	In	in	IN
10_1101-2020_10_26_351783	237	2	our	-PRON-	PRP$
10_1101-2020_10_26_351783	237	3	assessment	assessment	NN
10_1101-2020_10_26_351783	237	4	of	of	IN
10_1101-2020_10_26_351783	237	5	the	the	DT
10_1101-2020_10_26_351783	237	6	disease	disease	NN
10_1101-2020_10_26_351783	237	7	modules	module	NNS
10_1101-2020_10_26_351783	237	8	,	,	,
10_1101-2020_10_26_351783	237	9	methods	method	NNS
10_1101-2020_10_26_351783	237	10	such	such	JJ
10_1101-2020_10_26_351783	237	11	as	as	IN
10_1101-2020_10_26_351783	237	12	Clique	Clique	NNP
10_1101-2020_10_26_351783	237	13	SuM	sum	NN
10_1101-2020_10_26_351783	237	14	and	and	CC
10_1101-2020_10_26_351783	237	15	DIAMOnD	diamond	NN
10_1101-2020_10_26_351783	237	16	did	do	VBD
10_1101-2020_10_26_351783	237	17	perform	perform	VB
10_1101-2020_10_26_351783	237	18	better	well	RBR
10_1101-2020_10_26_351783	237	19	than	than	IN
10_1101-2020_10_26_351783	237	20	the	the	DT
10_1101-2020_10_26_351783	237	21	community	community	NN
10_1101-2020_10_26_351783	237	22	-	-	HYPH
10_1101-2020_10_26_351783	237	23	based	base	VBN
10_1101-2020_10_26_351783	237	24	consensus	consensus	NN
10_1101-2020_10_26_351783	237	25	predictions	prediction	NNS
10_1101-2020_10_26_351783	237	26	.	.	.
10_1101-2020_10_26_351783	238	1	In	in	IN
10_1101-2020_10_26_351783	238	2	summary	summary	NN
10_1101-2020_10_26_351783	238	3	,	,	,
10_1101-2020_10_26_351783	238	4	our	-PRON-	PRP$
10_1101-2020_10_26_351783	238	5	study	study	NN
10_1101-2020_10_26_351783	238	6	provides	provide	VBZ
10_1101-2020_10_26_351783	238	7	a	a	DT
10_1101-2020_10_26_351783	238	8	practical	practical	JJ
10_1101-2020_10_26_351783	238	9	integrative	integrative	JJ
10_1101-2020_10_26_351783	238	10	workflow	workflow	NN
10_1101-2020_10_26_351783	238	11	that	that	WDT
10_1101-2020_10_26_351783	238	12	enables	enable	VBZ
10_1101-2020_10_26_351783	238	13	system	system	NN
10_1101-2020_10_26_351783	238	14	-	-	HYPH
10_1101-2020_10_26_351783	238	15	level	level	NN
10_1101-2020_10_26_351783	238	16	analysis	analysis	NN
10_1101-2020_10_26_351783	238	17	of	of	IN
10_1101-2020_10_26_351783	238	18	heterogeneous	heterogeneous	JJ
10_1101-2020_10_26_351783	238	19	diseases	disease	NNS
10_1101-2020_10_26_351783	238	20	,	,	,
10_1101-2020_10_26_351783	238	21	in	in	IN
10_1101-2020_10_26_351783	238	22	terms	term	NNS
10_1101-2020_10_26_351783	238	23	of	of	IN
10_1101-2020_10_26_351783	238	24	multi	multi	JJ
10_1101-2020_10_26_351783	238	25	-	-	HYPH
10_1101-2020_10_26_351783	238	26	omics	omics	NNP
10_1101-2020_10_26_351783	238	27	disease	disease	NN
10_1101-2020_10_26_351783	238	28	modules	module	NNS
10_1101-2020_10_26_351783	238	29	,	,	,
10_1101-2020_10_26_351783	238	30	as	as	RB
10_1101-2020_10_26_351783	238	31	well	well	RB
10_1101-2020_10_26_351783	238	32	as	as	IN
10_1101-2020_10_26_351783	238	33	the	the	DT
10_1101-2020_10_26_351783	238	34	validation	validation	NN
10_1101-2020_10_26_351783	238	35	of	of	IN
10_1101-2020_10_26_351783	238	36	these	these	DT
10_1101-2020_10_26_351783	238	37	by	by	IN
10_1101-2020_10_26_351783	238	38	using	use	VBG
10_1101-2020_10_26_351783	238	39	both	both	DT
10_1101-2020_10_26_351783	238	40	disease	disease	NN
10_1101-2020_10_26_351783	238	41	-	-	HYPH
10_1101-2020_10_26_351783	238	42	specific	specific	JJ
10_1101-2020_10_26_351783	238	43	GWAS	gwas	NN
10_1101-2020_10_26_351783	238	44	and	and	CC
10_1101-2020_10_26_351783	238	45	risk	risk	NN
10_1101-2020_10_26_351783	238	46	factors	factor	NNS
10_1101-2020_10_26_351783	238	47	enrichment	enrichment	NN
10_1101-2020_10_26_351783	238	48	.	.	.
10_1101-2020_10_26_351783	239	1	We	-PRON-	PRP
10_1101-2020_10_26_351783	239	2	believe	believe	VBP
10_1101-2020_10_26_351783	239	3	that	that	IN
10_1101-2020_10_26_351783	239	4	this	this	DT
10_1101-2020_10_26_351783	239	5	analysis	analysis	NN
10_1101-2020_10_26_351783	239	6	(	(	-LRB-
10_1101-2020_10_26_351783	239	7	which	which	WDT
10_1101-2020_10_26_351783	239	8	was	be	VBD
10_1101-2020_10_26_351783	239	9	not	not	RB
10_1101-2020_10_26_351783	239	10	certified	certify	VBN
10_1101-2020_10_26_351783	239	11	by	by	IN
10_1101-2020_10_26_351783	239	12	peer	peer	NN
10_1101-2020_10_26_351783	239	13	review	review	NN
10_1101-2020_10_26_351783	239	14	)	)	-RRB-
10_1101-2020_10_26_351783	239	15	is	be	VBZ
10_1101-2020_10_26_351783	239	16	the	the	DT
10_1101-2020_10_26_351783	239	17	author	author	NN
10_1101-2020_10_26_351783	239	18	/	/	SYM
10_1101-2020_10_26_351783	239	19	funder	funder	NN
10_1101-2020_10_26_351783	239	20	.	.	.
10_1101-2020_10_26_351783	240	1	All	all	DT
10_1101-2020_10_26_351783	240	2	rights	right	NNS
10_1101-2020_10_26_351783	240	3	reserved	reserve	VBD
10_1101-2020_10_26_351783	240	4	.	.	.
10_1101-2020_10_26_351783	241	1	No	no	DT
10_1101-2020_10_26_351783	241	2	reuse	reuse	NN
10_1101-2020_10_26_351783	241	3	allowed	allow	VBN
10_1101-2020_10_26_351783	241	4	without	without	IN
10_1101-2020_10_26_351783	241	5	permission	permission	NN
10_1101-2020_10_26_351783	241	6	.	.	.
10_1101-2020_10_26_351783	242	1	The	the	DT
10_1101-2020_10_26_351783	242	2	copyright	copyright	NN
10_1101-2020_10_26_351783	242	3	holder	holder	NN
10_1101-2020_10_26_351783	242	4	for	for	IN
10_1101-2020_10_26_351783	242	5	this	this	DT
10_1101-2020_10_26_351783	242	6	preprintthis	preprintthis	NN
10_1101-2020_10_26_351783	242	7	version	version	NN
10_1101-2020_10_26_351783	242	8	posted	post	VBD
10_1101-2020_10_26_351783	242	9	January	January	NNP
10_1101-2020_10_26_351783	242	10	6	6	CD
10_1101-2020_10_26_351783	242	11	,	,	,
10_1101-2020_10_26_351783	242	12	2021	2021	CD
10_1101-2020_10_26_351783	242	13	.	.	.
10_1101-2020_10_26_351783	242	14	;	;	:
10_1101-2020_10_26_351783	242	15	https://doi.org/10.1101/2020.10.26.351783doi	https://doi.org/10.1101/2020.10.26.351783doi	ADD
10_1101-2020_10_26_351783	242	16	:	:	:
10_1101-2020_10_26_351783	242	17	bioRxiv	biorxiv	VB
10_1101-2020_10_26_351783	242	18	preprint	preprint	NN
10_1101-2020_10_26_351783	242	19	https://doi.org/10.1101/2020.10.26.351783	https://doi.org/10.1101/2020.10.26.351783	CC
10_1101-2020_10_26_351783	242	20	17	17	CD
10_1101-2020_10_26_351783	242	21	validates	validate	NNS
10_1101-2020_10_26_351783	242	22	our	-PRON-	PRP$
10_1101-2020_10_26_351783	242	23	integrated	integrated	JJ
10_1101-2020_10_26_351783	242	24	use	use	NN
10_1101-2020_10_26_351783	242	25	datasets	dataset	NNS
10_1101-2020_10_26_351783	242	26	and	and	CC
10_1101-2020_10_26_351783	242	27	suggest	suggest	VBP
10_1101-2020_10_26_351783	242	28	a	a	DT
10_1101-2020_10_26_351783	242	29	pipeline	pipeline	NN
10_1101-2020_10_26_351783	242	30	that	that	WDT
10_1101-2020_10_26_351783	242	31	readily	readily	RB
10_1101-2020_10_26_351783	242	32	could	could	MD
10_1101-2020_10_26_351783	242	33	be	be	VB
10_1101-2020_10_26_351783	242	34	tested	test	VBN
10_1101-2020_10_26_351783	242	35	in	in	RP
10_1101-2020_10_26_351783	242	36	at	at	RB
10_1101-2020_10_26_351783	242	37	least	least	JJS
10_1101-2020_10_26_351783	242	38	in	in	IN
10_1101-2020_10_26_351783	242	39	other	other	JJ
10_1101-2020_10_26_351783	242	40	autoimmune	autoimmune	JJ
10_1101-2020_10_26_351783	242	41	and	and	CC
10_1101-2020_10_26_351783	242	42	cardiovascular	cardiovascular	JJ
10_1101-2020_10_26_351783	242	43	diseases	disease	NNS
10_1101-2020_10_26_351783	242	44	.	.	.
10_1101-2020_10_26_351783	243	1	Lastly	lastly	RB
10_1101-2020_10_26_351783	243	2	,	,	,
10_1101-2020_10_26_351783	243	3	our	-PRON-	PRP$
10_1101-2020_10_26_351783	243	4	study	study	NN
10_1101-2020_10_26_351783	243	5	did	do	VBD
10_1101-2020_10_26_351783	243	6	not	not	RB
10_1101-2020_10_26_351783	243	7	aim	aim	VB
10_1101-2020_10_26_351783	243	8	to	to	TO
10_1101-2020_10_26_351783	243	9	optimize	optimize	VB
10_1101-2020_10_26_351783	243	10	hyper-	hyper-	JJ
10_1101-2020_10_26_351783	243	11	parameters	parameter	NNS
10_1101-2020_10_26_351783	243	12	for	for	IN
10_1101-2020_10_26_351783	243	13	individual	individual	JJ
10_1101-2020_10_26_351783	243	14	disease	disease	NN
10_1101-2020_10_26_351783	243	15	modules	module	NNS
10_1101-2020_10_26_351783	243	16	,	,	,
10_1101-2020_10_26_351783	243	17	and	and	CC
10_1101-2020_10_26_351783	243	18	instead	instead	RB
10_1101-2020_10_26_351783	243	19	used	use	VBD
10_1101-2020_10_26_351783	243	20	default	default	NN
10_1101-2020_10_26_351783	243	21	values	value	NNS
10_1101-2020_10_26_351783	243	22	when	when	WRB
10_1101-2020_10_26_351783	243	23	possible	possible	JJ
10_1101-2020_10_26_351783	243	24	,	,	,
10_1101-2020_10_26_351783	243	25	and	and	CC
10_1101-2020_10_26_351783	243	26	to	to	IN
10_1101-2020_10_26_351783	243	27	the	the	DT
10_1101-2020_10_26_351783	243	28	methods	method	NNS
10_1101-2020_10_26_351783	243	29	from	from	IN
10_1101-2020_10_26_351783	243	30	the	the	DT
10_1101-2020_10_26_351783	243	31	MODifieR	MODifieR	NNP
10_1101-2020_10_26_351783	243	32	R	r	NN
10_1101-2020_10_26_351783	243	33	package	package	NN
10_1101-2020_10_26_351783	243	34	implementation	implementation	NN
10_1101-2020_10_26_351783	243	35	of	of	IN
10_1101-2020_10_26_351783	243	36	the	the	DT
10_1101-2020_10_26_351783	243	37	methods[13	methods[13	NNP
10_1101-2020_10_26_351783	243	38	]	]	-RRB-
10_1101-2020_10_26_351783	243	39	.	.	.
10_1101-2020_10_26_351783	244	1	However	however	RB
10_1101-2020_10_26_351783	244	2	,	,	,
10_1101-2020_10_26_351783	244	3	this	this	DT
10_1101-2020_10_26_351783	244	4	might	may	MD
10_1101-2020_10_26_351783	244	5	be	be	VB
10_1101-2020_10_26_351783	244	6	an	an	DT
10_1101-2020_10_26_351783	244	7	important	important	JJ
10_1101-2020_10_26_351783	244	8	task	task	NN
10_1101-2020_10_26_351783	244	9	for	for	IN
10_1101-2020_10_26_351783	244	10	specific	specific	JJ
10_1101-2020_10_26_351783	244	11	disease	disease	NN
10_1101-2020_10_26_351783	244	12	and	and	CC
10_1101-2020_10_26_351783	244	13	our	-PRON-	PRP$
10_1101-2020_10_26_351783	244	14	code	code	NN
10_1101-2020_10_26_351783	244	15	and	and	CC
10_1101-2020_10_26_351783	244	16	processed	process	VBN
10_1101-2020_10_26_351783	244	17	datasets	dataset	NNS
10_1101-2020_10_26_351783	244	18	are	be	VBP
10_1101-2020_10_26_351783	244	19	available	available	JJ
10_1101-2020_10_26_351783	244	20	at	at	IN
10_1101-2020_10_26_351783	244	21	GitLab	GitLab	NNP
10_1101-2020_10_26_351783	244	22	(	(	-LRB-
10_1101-2020_10_26_351783	244	23	https://gitlab.com/Gustafsson-lab/modifier-benchmark	https://gitlab.com/Gustafsson-lab/modifier-benchmark	NNP
10_1101-2020_10_26_351783	244	24	)	)	-RRB-
10_1101-2020_10_26_351783	244	25	.	.	.
10_1101-2020_10_26_351783	245	1	In	in	IN
10_1101-2020_10_26_351783	245	2	future	future	JJ
10_1101-2020_10_26_351783	245	3	work	work	NN
10_1101-2020_10_26_351783	245	4	,	,	,
10_1101-2020_10_26_351783	245	5	this	this	DT
10_1101-2020_10_26_351783	245	6	approach	approach	NN
10_1101-2020_10_26_351783	245	7	can	can	MD
10_1101-2020_10_26_351783	245	8	be	be	VB
10_1101-2020_10_26_351783	245	9	expanded	expand	VBN
10_1101-2020_10_26_351783	245	10	to	to	TO
10_1101-2020_10_26_351783	245	11	include	include	VB
10_1101-2020_10_26_351783	245	12	diverse	diverse	JJ
10_1101-2020_10_26_351783	245	13	and	and	CC
10_1101-2020_10_26_351783	245	14	context	context	NN
10_1101-2020_10_26_351783	245	15	-	-	HYPH
10_1101-2020_10_26_351783	245	16	specific	specific	JJ
10_1101-2020_10_26_351783	245	17	networks	network	NNS
10_1101-2020_10_26_351783	245	18	to	to	TO
10_1101-2020_10_26_351783	245	19	determine	determine	VB
10_1101-2020_10_26_351783	245	20	whether	whether	IN
10_1101-2020_10_26_351783	245	21	our	-PRON-	PRP$
10_1101-2020_10_26_351783	245	22	multi	multi	JJ
10_1101-2020_10_26_351783	245	23	-	-	NNS
10_1101-2020_10_26_351783	245	24	omics	omics	JJ
10_1101-2020_10_26_351783	245	25	modules	module	NNS
10_1101-2020_10_26_351783	245	26	are	be	VBP
10_1101-2020_10_26_351783	245	27	able	able	JJ
10_1101-2020_10_26_351783	245	28	to	to	TO
10_1101-2020_10_26_351783	245	29	capture	capture	VB
10_1101-2020_10_26_351783	245	30	various	various	JJ
10_1101-2020_10_26_351783	245	31	other	other	JJ
10_1101-2020_10_26_351783	245	32	levels	level	NNS
10_1101-2020_10_26_351783	245	33	of	of	IN
10_1101-2020_10_26_351783	245	34	granularity	granularity	NN
10_1101-2020_10_26_351783	245	35	.	.	.
10_1101-2020_10_26_351783	246	1	(	(	-LRB-
10_1101-2020_10_26_351783	246	2	which	which	WDT
10_1101-2020_10_26_351783	246	3	was	be	VBD
10_1101-2020_10_26_351783	246	4	not	not	RB
10_1101-2020_10_26_351783	246	5	certified	certify	VBN
10_1101-2020_10_26_351783	246	6	by	by	IN
10_1101-2020_10_26_351783	246	7	peer	peer	NN
10_1101-2020_10_26_351783	246	8	review	review	NN
10_1101-2020_10_26_351783	246	9	)	)	-RRB-
10_1101-2020_10_26_351783	246	10	is	be	VBZ
10_1101-2020_10_26_351783	246	11	the	the	DT
10_1101-2020_10_26_351783	246	12	author	author	NN
10_1101-2020_10_26_351783	246	13	/	/	SYM
10_1101-2020_10_26_351783	246	14	funder	funder	NN
10_1101-2020_10_26_351783	246	15	.	.	.
10_1101-2020_10_26_351783	247	1	All	all	DT
10_1101-2020_10_26_351783	247	2	rights	right	NNS
10_1101-2020_10_26_351783	247	3	reserved	reserve	VBD
10_1101-2020_10_26_351783	247	4	.	.	.
10_1101-2020_10_26_351783	248	1	No	no	DT
10_1101-2020_10_26_351783	248	2	reuse	reuse	NN
10_1101-2020_10_26_351783	248	3	allowed	allow	VBN
10_1101-2020_10_26_351783	248	4	without	without	IN
10_1101-2020_10_26_351783	248	5	permission	permission	NN
10_1101-2020_10_26_351783	248	6	.	.	.
10_1101-2020_10_26_351783	249	1	The	the	DT
10_1101-2020_10_26_351783	249	2	copyright	copyright	NN
10_1101-2020_10_26_351783	249	3	holder	holder	NN
10_1101-2020_10_26_351783	249	4	for	for	IN
10_1101-2020_10_26_351783	249	5	this	this	DT
10_1101-2020_10_26_351783	249	6	preprintthis	preprintthis	NN
10_1101-2020_10_26_351783	249	7	version	version	NN
10_1101-2020_10_26_351783	249	8	posted	post	VBD
10_1101-2020_10_26_351783	249	9	January	January	NNP
10_1101-2020_10_26_351783	249	10	6	6	CD
10_1101-2020_10_26_351783	249	11	,	,	,
10_1101-2020_10_26_351783	249	12	2021	2021	CD
10_1101-2020_10_26_351783	249	13	.	.	.
10_1101-2020_10_26_351783	249	14	;	;	:
10_1101-2020_10_26_351783	249	15	https://doi.org/10.1101/2020.10.26.351783doi	https://doi.org/10.1101/2020.10.26.351783doi	ADD
10_1101-2020_10_26_351783	249	16	:	:	:
10_1101-2020_10_26_351783	249	17	bioRxiv	biorxiv	IN
10_1101-2020_10_26_351783	249	18	preprint	preprint	NN
10_1101-2020_10_26_351783	249	19	https://doi.org/10.1101/2020.10.26.351783	https://doi.org/10.1101/2020.10.26.351783	CC
10_1101-2020_10_26_351783	249	20	18	18	CD
10_1101-2020_10_26_351783	249	21	DECLARATIONS	declaration	NNS
10_1101-2020_10_26_351783	249	22	ETHICS	ethic	NNS
10_1101-2020_10_26_351783	249	23	APPROVAL	APPROVAL	NNS
10_1101-2020_10_26_351783	249	24	AND	and	CC
10_1101-2020_10_26_351783	249	25	CONSENT	consent	VBP
10_1101-2020_10_26_351783	249	26	TO	to	TO
10_1101-2020_10_26_351783	249	27	PARTICIPATE	PARTICIPATE	NNP
10_1101-2020_10_26_351783	249	28	Not	not	RB
10_1101-2020_10_26_351783	249	29	applicable	applicable	JJ
10_1101-2020_10_26_351783	249	30	AVAILABILITY	AVAILABILITY	NNS
10_1101-2020_10_26_351783	249	31	OF	of	IN
10_1101-2020_10_26_351783	249	32	DATA	datum	NNS
10_1101-2020_10_26_351783	249	33	AND	and	CC
10_1101-2020_10_26_351783	249	34	MATERIALS	material	NNS
10_1101-2020_10_26_351783	249	35	The	the	DT
10_1101-2020_10_26_351783	249	36	data	datum	NNS
10_1101-2020_10_26_351783	249	37	used	use	VBN
10_1101-2020_10_26_351783	249	38	for	for	IN
10_1101-2020_10_26_351783	249	39	transcriptomic	transcriptomic	NNP
10_1101-2020_10_26_351783	249	40	benchmark	benchmark	NN
10_1101-2020_10_26_351783	249	41	and	and	CC
10_1101-2020_10_26_351783	249	42	methylation	methylation	NN
10_1101-2020_10_26_351783	249	43	benchmark	benchmark	NN
10_1101-2020_10_26_351783	249	44	are	be	VBP
10_1101-2020_10_26_351783	249	45	downloaded	download	VBN
10_1101-2020_10_26_351783	249	46	from	from	IN
10_1101-2020_10_26_351783	249	47	GEO	GEO	NNP
10_1101-2020_10_26_351783	249	48	.	.	.
10_1101-2020_10_26_351783	250	1	The	the	DT
10_1101-2020_10_26_351783	250	2	disease	disease	NN
10_1101-2020_10_26_351783	250	3	specific	specific	JJ
10_1101-2020_10_26_351783	250	4	GWAS	GWAS	NNP
10_1101-2020_10_26_351783	250	5	files	file	NNS
10_1101-2020_10_26_351783	250	6	are	be	VBP
10_1101-2020_10_26_351783	250	7	downloaded	download	VBN
10_1101-2020_10_26_351783	250	8	from	from	IN
10_1101-2020_10_26_351783	250	9	the	the	DT
10_1101-2020_10_26_351783	250	10	latest	late	JJS
10_1101-2020_10_26_351783	250	11	Pascal	pascal	JJ
10_1101-2020_10_26_351783	250	12	version	version	NN
10_1101-2020_10_26_351783	250	13	.	.	.
10_1101-2020_10_26_351783	251	1	The	the	DT
10_1101-2020_10_26_351783	251	2	processed	process	VBN
10_1101-2020_10_26_351783	251	3	Data	Data	NNP
10_1101-2020_10_26_351783	251	4	for	for	IN
10_1101-2020_10_26_351783	251	5	analysis	analysis	NN
10_1101-2020_10_26_351783	251	6	is	be	VBZ
10_1101-2020_10_26_351783	251	7	available	available	JJ
10_1101-2020_10_26_351783	251	8	at	at	IN
10_1101-2020_10_26_351783	251	9	https://gitlab.com/Gustafsson-lab/modifier-benchmark.The	https://gitlab.com/gustafsson-lab/modifier-benchmark.the	CD
10_1101-2020_10_26_351783	251	10	risk	risk	NN
10_1101-2020_10_26_351783	251	11	factor	factor	NN
10_1101-2020_10_26_351783	251	12	(	(	-LRB-
10_1101-2020_10_26_351783	251	13	EIMS	eims	NN
10_1101-2020_10_26_351783	251	14	)	)	-RRB-
10_1101-2020_10_26_351783	251	15	data	datum	NNS
10_1101-2020_10_26_351783	251	16	will	will	MD
10_1101-2020_10_26_351783	251	17	be	be	VB
10_1101-2020_10_26_351783	251	18	made	make	VBN
10_1101-2020_10_26_351783	251	19	available	available	JJ
10_1101-2020_10_26_351783	251	20	on	on	IN
10_1101-2020_10_26_351783	251	21	request	request	NN
10_1101-2020_10_26_351783	251	22	.	.	.
10_1101-2020_10_26_351783	252	1	The	the	DT
10_1101-2020_10_26_351783	252	2	R	r	NN
10_1101-2020_10_26_351783	252	3	-	-	HYPH
10_1101-2020_10_26_351783	252	4	package	package	NN
10_1101-2020_10_26_351783	252	5	MODifieR	modifier	NN
10_1101-2020_10_26_351783	252	6	is	be	VBZ
10_1101-2020_10_26_351783	252	7	available	available	JJ
10_1101-2020_10_26_351783	252	8	on	on	IN
10_1101-2020_10_26_351783	252	9	the	the	DT
10_1101-2020_10_26_351783	252	10	GitLab	GitLab	NNP
10_1101-2020_10_26_351783	252	11	:	:	:
10_1101-2020_10_26_351783	252	12	https://gitlab.com/Gustafsson-lab/MODifieR	https://gitlab.com/Gustafsson-lab/MODifieR	NNP
10_1101-2020_10_26_351783	252	13	;	;	:
10_1101-2020_10_26_351783	252	14	the	the	DT
10_1101-2020_10_26_351783	252	15	code	code	NN
10_1101-2020_10_26_351783	252	16	used	use	VBN
10_1101-2020_10_26_351783	252	17	for	for	IN
10_1101-2020_10_26_351783	252	18	benchmark	benchmark	JJ
10_1101-2020_10_26_351783	252	19	analysis	analysis	NN
10_1101-2020_10_26_351783	252	20	and	and	CC
10_1101-2020_10_26_351783	252	21	risk	risk	NN
10_1101-2020_10_26_351783	252	22	factor	factor	NN
10_1101-2020_10_26_351783	252	23	analysis	analysis	NN
10_1101-2020_10_26_351783	252	24	is	be	VBZ
10_1101-2020_10_26_351783	252	25	available	available	JJ
10_1101-2020_10_26_351783	252	26	on	on	IN
10_1101-2020_10_26_351783	252	27	GitLab	GitLab	NNP
10_1101-2020_10_26_351783	252	28	:	:	:
10_1101-2020_10_26_351783	252	29	https://gitlab.com/Gustafsson-lab/modifier-benchmark	https://gitlab.com/gustafsson-lab/modifier-benchmark	NN
10_1101-2020_10_26_351783	252	30	;	;	:
10_1101-2020_10_26_351783	252	31	the	the	DT
10_1101-2020_10_26_351783	252	32	latest	late	JJS
10_1101-2020_10_26_351783	252	33	Pascal	pascal	JJ
10_1101-2020_10_26_351783	252	34	version	version	NN
10_1101-2020_10_26_351783	252	35	:	:	:
10_1101-2020_10_26_351783	252	36	https://www2.unil.ch/cbg/index.php?title=Pascal	https://www2.unil.ch/cbg/index.php?title=pascal	JJ
10_1101-2020_10_26_351783	252	37	.	.	.
10_1101-2020_10_26_351783	253	1	COMPETING	compete	VBG
10_1101-2020_10_26_351783	253	2	INTERESTS	INTERESTS	NNP
10_1101-2020_10_26_351783	253	3	The	the	DT
10_1101-2020_10_26_351783	253	4	authors	author	NNS
10_1101-2020_10_26_351783	253	5	declare	declare	VBP
10_1101-2020_10_26_351783	253	6	no	no	DT
10_1101-2020_10_26_351783	253	7	competing	compete	VBG
10_1101-2020_10_26_351783	253	8	interests	interest	NNS
10_1101-2020_10_26_351783	253	9	.	.	.
10_1101-2020_10_26_351783	254	1	FUNDING	fund	VBG
10_1101-2020_10_26_351783	254	2	This	this	DT
10_1101-2020_10_26_351783	254	3	work	work	NN
10_1101-2020_10_26_351783	254	4	was	be	VBD
10_1101-2020_10_26_351783	254	5	supported	support	VBN
10_1101-2020_10_26_351783	254	6	by	by	IN
10_1101-2020_10_26_351783	254	7	the	the	DT
10_1101-2020_10_26_351783	254	8	Swedish	Swedish	NNP
10_1101-2020_10_26_351783	254	9	Research	Research	NNP
10_1101-2020_10_26_351783	254	10	Council	Council	NNP
10_1101-2020_10_26_351783	254	11	(	(	-LRB-
10_1101-2020_10_26_351783	254	12	grant	grant	NNP
10_1101-2020_10_26_351783	254	13	2015	2015	CD
10_1101-2020_10_26_351783	254	14	-	-	HYPH
10_1101-2020_10_26_351783	254	15	03807(M.G.	03807(m.g.	CD
10_1101-2020_10_26_351783	255	1	)	)	-RRB-
10_1101-2020_10_26_351783	255	2	,	,	,
10_1101-2020_10_26_351783	255	3	grant	grant	VB
10_1101-2020_10_26_351783	255	4	2018-	2018-	CD
10_1101-2020_10_26_351783	255	5	02638(M.J.	02638(m.j.	CD
10_1101-2020_10_26_351783	255	6	)	)	-RRB-
10_1101-2020_10_26_351783	255	7	)	)	-RRB-
10_1101-2020_10_26_351783	255	8	,	,	,
10_1101-2020_10_26_351783	255	9	the	the	DT
10_1101-2020_10_26_351783	255	10	Swedish	swedish	JJ
10_1101-2020_10_26_351783	255	11	foundation	foundation	NN
10_1101-2020_10_26_351783	255	12	for	for	IN
10_1101-2020_10_26_351783	255	13	strategic	strategic	JJ
10_1101-2020_10_26_351783	255	14	research	research	NN
10_1101-2020_10_26_351783	255	15	(	(	-LRB-
10_1101-2020_10_26_351783	255	16	grant	grant	VB
10_1101-2020_10_26_351783	255	17	SB16	SB16	NNP
10_1101-2020_10_26_351783	255	18	-	-	HYPH
10_1101-2020_10_26_351783	255	19	0095(M.G.	0095(M.G.	NNP
10_1101-2020_10_26_351783	256	1	)	)	-RRB-
10_1101-2020_10_26_351783	256	2	)	)	-RRB-
10_1101-2020_10_26_351783	256	3	,	,	,
10_1101-2020_10_26_351783	256	4	the	the	DT
10_1101-2020_10_26_351783	256	5	Center	Center	NNP
10_1101-2020_10_26_351783	256	6	for	for	IN
10_1101-2020_10_26_351783	256	7	Industrial	Industrial	NNP
10_1101-2020_10_26_351783	256	8	IT	IT	NNP
10_1101-2020_10_26_351783	256	9	(	(	-LRB-
10_1101-2020_10_26_351783	256	10	CENIIT)(M.G.	CENIIT)(M.G.	.
10_1101-2020_10_26_351783	257	1	)	)	-RRB-
10_1101-2020_10_26_351783	257	2	,	,	,
10_1101-2020_10_26_351783	257	3	European	European	NNP
10_1101-2020_10_26_351783	257	4	Union	Union	NNP
10_1101-2020_10_26_351783	257	5	Horizon	Horizon	NNP
10_1101-2020_10_26_351783	257	6	2020	2020	CD
10_1101-2020_10_26_351783	257	7	/	/	SYM
10_1101-2020_10_26_351783	257	8	European	European	NNP
10_1101-2020_10_26_351783	257	9	Research	Research	NNP
10_1101-2020_10_26_351783	257	10	Council	Council	NNP
10_1101-2020_10_26_351783	257	11	Consolidator	Consolidator	NNP
10_1101-2020_10_26_351783	257	12	grant	grant	NN
10_1101-2020_10_26_351783	257	13	(	(	-LRB-
10_1101-2020_10_26_351783	257	14	Epi4MS	epi4ms	NN
10_1101-2020_10_26_351783	257	15	,	,	,
10_1101-2020_10_26_351783	257	16	grant	grant	VB
10_1101-2020_10_26_351783	257	17	818170(M.J.	818170(m.j.	CD
10_1101-2020_10_26_351783	257	18	)	)	-RRB-
10_1101-2020_10_26_351783	257	19	)	)	-RRB-
10_1101-2020_10_26_351783	257	20	,	,	,
10_1101-2020_10_26_351783	257	21	Knut	Knut	NNP
10_1101-2020_10_26_351783	257	22	and	and	CC
10_1101-2020_10_26_351783	257	23	Alice	Alice	NNP
10_1101-2020_10_26_351783	257	24	Wallenberg	Wallenberg	NNP
10_1101-2020_10_26_351783	257	25	Foundation	Foundation	NNP
10_1101-2020_10_26_351783	257	26	(	(	-LRB-
10_1101-2020_10_26_351783	257	27	grant	grant	NNP
10_1101-2020_10_26_351783	257	28	2019.0089(M.J.	2019.0089(m.j.	CD
10_1101-2020_10_26_351783	257	29	)	)	-RRB-
10_1101-2020_10_26_351783	257	30	)	)	-RRB-
10_1101-2020_10_26_351783	257	31	and	and	CC
10_1101-2020_10_26_351783	257	32	the	the	DT
10_1101-2020_10_26_351783	257	33	Knowledge	Knowledge	NNP
10_1101-2020_10_26_351783	257	34	Foundation	Foundation	NNP
10_1101-2020_10_26_351783	257	35	(	(	-LRB-
10_1101-2020_10_26_351783	257	36	grant	grant	NNP
10_1101-2020_10_26_351783	257	37	20170298	20170298	CD
10_1101-2020_10_26_351783	257	38	(	(	-LRB-
10_1101-2020_10_26_351783	257	39	Z.L.	Z.L.	NNP
10_1101-2020_10_26_351783	257	40	)	)	-RRB-
10_1101-2020_10_26_351783	257	41	)	)	-RRB-
10_1101-2020_10_26_351783	257	42	.	.	.
10_1101-2020_10_26_351783	258	1	Computational	computational	JJ
10_1101-2020_10_26_351783	258	2	resources	resource	NNS
10_1101-2020_10_26_351783	258	3	were	be	VBD
10_1101-2020_10_26_351783	258	4	granted	grant	VBN
10_1101-2020_10_26_351783	258	5	by	by	IN
10_1101-2020_10_26_351783	258	6	Swedish	swedish	JJ
10_1101-2020_10_26_351783	258	7	National	National	NNP
10_1101-2020_10_26_351783	258	8	Infrastructure	Infrastructure	NNP
10_1101-2020_10_26_351783	258	9	for	for	IN
10_1101-2020_10_26_351783	258	10	Computing	Computing	NNP
10_1101-2020_10_26_351783	258	11	(	(	-LRB-
10_1101-2020_10_26_351783	258	12	SNIC	SNIC	NNP
10_1101-2020_10_26_351783	258	13	;	;	:
10_1101-2020_10_26_351783	258	14	SNIC	SNIC	NNP
10_1101-2020_10_26_351783	258	15	2020/5	2020/5	CD
10_1101-2020_10_26_351783	258	16	-	-	SYM
10_1101-2020_10_26_351783	258	17	177	177	CD
10_1101-2020_10_26_351783	258	18	,	,	,
10_1101-2020_10_26_351783	258	19	LiU-2018	LiU-2018	NNP
10_1101-2020_10_26_351783	258	20	-	-	HYPH
10_1101-2020_10_26_351783	258	21	12	12	NNP
10_1101-2020_10_26_351783	258	22	and	and	CC
10_1101-2020_10_26_351783	258	23	LiU-2019-	LiU-2019-	NNP
10_1101-2020_10_26_351783	258	24	25	25	CD
10_1101-2020_10_26_351783	258	25	)	)	-RRB-
10_1101-2020_10_26_351783	258	26	.	.	.
10_1101-2020_10_26_351783	259	1	AUTHOR	AUTHOR	NNP
10_1101-2020_10_26_351783	259	2	CONTRIBUTIONS	contributions	NN
10_1101-2020_10_26_351783	259	3	T.V.S.B.	t.v.s.b.	NN
10_1101-2020_10_26_351783	260	1	compiled	compile	VBD
10_1101-2020_10_26_351783	260	2	the	the	DT
10_1101-2020_10_26_351783	260	3	necessary	necessary	JJ
10_1101-2020_10_26_351783	260	4	data	datum	NNS
10_1101-2020_10_26_351783	260	5	for	for	IN
10_1101-2020_10_26_351783	260	6	the	the	DT
10_1101-2020_10_26_351783	260	7	benchmark	benchmark	JJ
10_1101-2020_10_26_351783	260	8	analysis	analysis	NN
10_1101-2020_10_26_351783	260	9	.	.	.
10_1101-2020_10_26_351783	261	1	H.A.W.	H.A.W.	NNP
10_1101-2020_10_26_351783	262	1	performed	perform	VBD
10_1101-2020_10_26_351783	262	2	the	the	DT
10_1101-2020_10_26_351783	262	3	transcriptomic	transcriptomic	NNP
10_1101-2020_10_26_351783	262	4	benchmark	benchmark	NN
10_1101-2020_10_26_351783	262	5	analysis	analysis	NN
10_1101-2020_10_26_351783	262	6	.	.	.
10_1101-2020_10_26_351783	263	1	T.V.S.B.	t.v.s.b.	XX
10_1101-2020_10_26_351783	264	1	performed	perform	VBD
10_1101-2020_10_26_351783	264	2	the	the	DT
10_1101-2020_10_26_351783	264	3	methylation	methylation	NN
10_1101-2020_10_26_351783	264	4	benchmark	benchmark	NN
10_1101-2020_10_26_351783	264	5	analysis	analysis	NN
10_1101-2020_10_26_351783	264	6	.	.	.
10_1101-2020_10_26_351783	265	1	D.M.E.	D.M.E.	NNP
10_1101-2020_10_26_351783	266	1	and	and	CC
10_1101-2020_10_26_351783	266	2	H.A.W.	H.A.W.	NNP
10_1101-2020_10_26_351783	267	1	performed	perform	VBD
10_1101-2020_10_26_351783	267	2	the	the	DT
10_1101-2020_10_26_351783	267	3	MS	MS	NNP
10_1101-2020_10_26_351783	267	4	use	use	VB
10_1101-2020_10_26_351783	267	5	case	case	NN
10_1101-2020_10_26_351783	267	6	analysis	analysis	NN
10_1101-2020_10_26_351783	267	7	.	.	.
10_1101-2020_10_26_351783	268	1	D.M.E	D.M.E	NNP
10_1101-2020_10_26_351783	268	2	performed	perform	VBD
10_1101-2020_10_26_351783	268	3	the	the	DT
10_1101-2020_10_26_351783	268	4	risk	risk	NN
10_1101-2020_10_26_351783	268	5	factor	factor	NN
10_1101-2020_10_26_351783	268	6	analysis	analysis	NN
10_1101-2020_10_26_351783	268	7	.	.	.
10_1101-2020_10_26_351783	269	1	(	(	-LRB-
10_1101-2020_10_26_351783	269	2	which	which	WDT
10_1101-2020_10_26_351783	269	3	was	be	VBD
10_1101-2020_10_26_351783	269	4	not	not	RB
10_1101-2020_10_26_351783	269	5	certified	certify	VBN
10_1101-2020_10_26_351783	269	6	by	by	IN
10_1101-2020_10_26_351783	269	7	peer	peer	NN
10_1101-2020_10_26_351783	269	8	review	review	NN
10_1101-2020_10_26_351783	269	9	)	)	-RRB-
10_1101-2020_10_26_351783	269	10	is	be	VBZ
10_1101-2020_10_26_351783	269	11	the	the	DT
10_1101-2020_10_26_351783	269	12	author	author	NN
10_1101-2020_10_26_351783	269	13	/	/	SYM
10_1101-2020_10_26_351783	269	14	funder	funder	NN
10_1101-2020_10_26_351783	269	15	.	.	.
10_1101-2020_10_26_351783	270	1	All	all	DT
10_1101-2020_10_26_351783	270	2	rights	right	NNS
10_1101-2020_10_26_351783	270	3	reserved	reserve	VBD
10_1101-2020_10_26_351783	270	4	.	.	.
10_1101-2020_10_26_351783	271	1	No	no	DT
10_1101-2020_10_26_351783	271	2	reuse	reuse	NN
10_1101-2020_10_26_351783	271	3	allowed	allow	VBN
10_1101-2020_10_26_351783	271	4	without	without	IN
10_1101-2020_10_26_351783	271	5	permission	permission	NN
10_1101-2020_10_26_351783	271	6	.	.	.
10_1101-2020_10_26_351783	272	1	The	the	DT
10_1101-2020_10_26_351783	272	2	copyright	copyright	NN
10_1101-2020_10_26_351783	272	3	holder	holder	NN
10_1101-2020_10_26_351783	272	4	for	for	IN
10_1101-2020_10_26_351783	272	5	this	this	DT
10_1101-2020_10_26_351783	272	6	preprintthis	preprintthis	NN
10_1101-2020_10_26_351783	272	7	version	version	NN
10_1101-2020_10_26_351783	272	8	posted	post	VBD
10_1101-2020_10_26_351783	272	9	January	January	NNP
10_1101-2020_10_26_351783	272	10	6	6	CD
10_1101-2020_10_26_351783	272	11	,	,	,
10_1101-2020_10_26_351783	272	12	2021	2021	CD
10_1101-2020_10_26_351783	272	13	.	.	.
10_1101-2020_10_26_351783	272	14	;	;	:
10_1101-2020_10_26_351783	272	15	https://doi.org/10.1101/2020.10.26.351783doi	https://doi.org/10.1101/2020.10.26.351783doi	ADD
10_1101-2020_10_26_351783	272	16	:	:	:
10_1101-2020_10_26_351783	272	17	bioRxiv	biorxiv	IN
10_1101-2020_10_26_351783	272	18	preprint	preprint	NN
10_1101-2020_10_26_351783	272	19	https://doi.org/10.1101/2020.10.26.351783	https://doi.org/10.1101/2020.10.26.351783	CC
10_1101-2020_10_26_351783	272	20	19	19	CD
10_1101-2020_10_26_351783	272	21	M.J.,I.K.,T.O.	m.j.,i.k.,t.o.	CD
10_1101-2020_10_26_351783	272	22	,	,	,
10_1101-2020_10_26_351783	272	23	and	and	CC
10_1101-2020_10_26_351783	272	24	L.A.	L.A.	NNP
10_1101-2020_10_26_351783	272	25	,	,	,
10_1101-2020_10_26_351783	272	26	provided	provide	VBD
10_1101-2020_10_26_351783	272	27	the	the	DT
10_1101-2020_10_26_351783	272	28	raw	raw	JJ
10_1101-2020_10_26_351783	272	29	data	datum	NNS
10_1101-2020_10_26_351783	272	30	and	and	CC
10_1101-2020_10_26_351783	272	31	collected	collect	VBD
10_1101-2020_10_26_351783	272	32	the	the	DT
10_1101-2020_10_26_351783	272	33	associated	associate	VBN
10_1101-2020_10_26_351783	272	34	risk	risk	NN
10_1101-2020_10_26_351783	272	35	factor	factor	NN
10_1101-2020_10_26_351783	272	36	data	datum	NNS
10_1101-2020_10_26_351783	272	37	for	for	IN
10_1101-2020_10_26_351783	272	38	the	the	DT
10_1101-2020_10_26_351783	272	39	independent	independent	JJ
10_1101-2020_10_26_351783	272	40	methylation	methylation	NN
10_1101-2020_10_26_351783	272	41	dataset	dataset	NN
10_1101-2020_10_26_351783	272	42	.	.	.
10_1101-2020_10_26_351783	273	1	T.V.S.B	T.V.S.B	NNP
10_1101-2020_10_26_351783	273	2	performed	perform	VBD
10_1101-2020_10_26_351783	273	3	the	the	DT
10_1101-2020_10_26_351783	273	4	independent	independent	JJ
10_1101-2020_10_26_351783	273	5	validation	validation	NN
10_1101-2020_10_26_351783	273	6	dataset	dataset	NN
10_1101-2020_10_26_351783	273	7	analysis	analysis	NN
10_1101-2020_10_26_351783	273	8	.	.	.
10_1101-2020_10_26_351783	274	1	T.V.S.B.	t.v.s.b.	XX
10_1101-2020_10_26_351783	275	1	and	and	CC
10_1101-2020_10_26_351783	275	2	D.M.E.	D.M.E.	NNP
10_1101-2020_10_26_351783	276	1	collectively	collectively	RB
10_1101-2020_10_26_351783	276	2	made	make	VBD
10_1101-2020_10_26_351783	276	3	the	the	DT
10_1101-2020_10_26_351783	276	4	plots	plot	NNS
10_1101-2020_10_26_351783	276	5	and	and	CC
10_1101-2020_10_26_351783	276	6	figures	figure	NNS
10_1101-2020_10_26_351783	276	7	for	for	IN
10_1101-2020_10_26_351783	276	8	the	the	DT
10_1101-2020_10_26_351783	276	9	manuscript	manuscript	NN
10_1101-2020_10_26_351783	276	10	.	.	.
10_1101-2020_10_26_351783	277	1	M.G.	M.G.	NNP
10_1101-2020_10_26_351783	278	1	and	and	CC
10_1101-2020_10_26_351783	278	2	Z.L.	Z.L.	NNP
10_1101-2020_10_26_351783	279	1	designed	design	VBN
10_1101-2020_10_26_351783	279	2	the	the	DT
10_1101-2020_10_26_351783	279	3	study	study	NN
10_1101-2020_10_26_351783	279	4	.	.	.
10_1101-2020_10_26_351783	280	1	T.V.S.B.	t.v.s.b.	XX
10_1101-2020_10_26_351783	281	1	and	and	CC
10_1101-2020_10_26_351783	281	2	D.M.E.	D.M.E.	NNP
10_1101-2020_10_26_351783	282	1	prepared	prepare	VBD
10_1101-2020_10_26_351783	282	2	the	the	DT
10_1101-2020_10_26_351783	282	3	manuscript	manuscript	NN
10_1101-2020_10_26_351783	282	4	.	.	.
10_1101-2020_10_26_351783	283	1	All	all	DT
10_1101-2020_10_26_351783	283	2	authors	author	NNS
10_1101-2020_10_26_351783	283	3	discussed	discuss	VBD
10_1101-2020_10_26_351783	283	4	the	the	DT
10_1101-2020_10_26_351783	283	5	results	result	NNS
10_1101-2020_10_26_351783	283	6	and	and	CC
10_1101-2020_10_26_351783	283	7	commented	comment	VBN
10_1101-2020_10_26_351783	283	8	on	on	IN
10_1101-2020_10_26_351783	283	9	the	the	DT
10_1101-2020_10_26_351783	283	10	manuscript	manuscript	NN
10_1101-2020_10_26_351783	283	11	at	at	IN
10_1101-2020_10_26_351783	283	12	all	all	DT
10_1101-2020_10_26_351783	283	13	stages	stage	NNS
10_1101-2020_10_26_351783	283	14	.	.	.
10_1101-2020_10_26_351783	284	1	(	(	-LRB-
10_1101-2020_10_26_351783	284	2	which	which	WDT
10_1101-2020_10_26_351783	284	3	was	be	VBD
10_1101-2020_10_26_351783	284	4	not	not	RB
10_1101-2020_10_26_351783	284	5	certified	certify	VBN
10_1101-2020_10_26_351783	284	6	by	by	IN
10_1101-2020_10_26_351783	284	7	peer	peer	NN
10_1101-2020_10_26_351783	284	8	review	review	NN
10_1101-2020_10_26_351783	284	9	)	)	-RRB-
10_1101-2020_10_26_351783	284	10	is	be	VBZ
10_1101-2020_10_26_351783	284	11	the	the	DT
10_1101-2020_10_26_351783	284	12	author	author	NN
10_1101-2020_10_26_351783	284	13	/	/	SYM
10_1101-2020_10_26_351783	284	14	funder	funder	NN
10_1101-2020_10_26_351783	284	15	.	.	.
10_1101-2020_10_26_351783	285	1	All	all	DT
10_1101-2020_10_26_351783	285	2	rights	right	NNS
10_1101-2020_10_26_351783	285	3	reserved	reserve	VBD
10_1101-2020_10_26_351783	285	4	.	.	.
10_1101-2020_10_26_351783	286	1	No	no	DT
10_1101-2020_10_26_351783	286	2	reuse	reuse	NN
10_1101-2020_10_26_351783	286	3	allowed	allow	VBN
10_1101-2020_10_26_351783	286	4	without	without	IN
10_1101-2020_10_26_351783	286	5	permission	permission	NN
10_1101-2020_10_26_351783	286	6	.	.	.
10_1101-2020_10_26_351783	287	1	The	the	DT
10_1101-2020_10_26_351783	287	2	copyright	copyright	NN
10_1101-2020_10_26_351783	287	3	holder	holder	NN
10_1101-2020_10_26_351783	287	4	for	for	IN
10_1101-2020_10_26_351783	287	5	this	this	DT
10_1101-2020_10_26_351783	287	6	preprintthis	preprintthis	NN
10_1101-2020_10_26_351783	287	7	version	version	NN
10_1101-2020_10_26_351783	287	8	posted	post	VBD
10_1101-2020_10_26_351783	287	9	January	January	NNP
10_1101-2020_10_26_351783	287	10	6	6	CD
10_1101-2020_10_26_351783	287	11	,	,	,
10_1101-2020_10_26_351783	287	12	2021	2021	CD
10_1101-2020_10_26_351783	287	13	.	.	.
10_1101-2020_10_26_351783	287	14	;	;	:
10_1101-2020_10_26_351783	287	15	https://doi.org/10.1101/2020.10.26.351783doi	https://doi.org/10.1101/2020.10.26.351783doi	ADD
10_1101-2020_10_26_351783	287	16	:	:	:
10_1101-2020_10_26_351783	287	17	bioRxiv	biorxiv	VB
10_1101-2020_10_26_351783	287	18	preprint	preprint	NN
10_1101-2020_10_26_351783	287	19	https://doi.org/10.1101/2020.10.26.351783	https://doi.org/10.1101/2020.10.26.351783	CC
10_1101-2020_10_26_351783	287	20	20	20	CD
10_1101-2020_10_26_351783	287	21	REFERENCES	reference	NNS
10_1101-2020_10_26_351783	287	22	1	1	CD
10_1101-2020_10_26_351783	287	23	.	.	.
10_1101-2020_10_26_351783	288	1	Naylor	Naylor	NNP
10_1101-2020_10_26_351783	288	2	S	S	NNP
10_1101-2020_10_26_351783	288	3	,	,	,
10_1101-2020_10_26_351783	288	4	Chen	Chen	NNP
10_1101-2020_10_26_351783	288	5	JY	JY	NNP
10_1101-2020_10_26_351783	288	6	.	.	.
10_1101-2020_10_26_351783	289	1	NIH	NIH	NNP
10_1101-2020_10_26_351783	289	2	Public	Public	NNP
10_1101-2020_10_26_351783	289	3	Access	Access	NNP
10_1101-2020_10_26_351783	289	4	.	.	.
10_1101-2020_10_26_351783	290	1	Natl	Natl	NNP
10_1101-2020_10_26_351783	290	2	Institutes	Institutes	NNP
10_1101-2020_10_26_351783	290	3	Heal	Heal	NNP
10_1101-2020_10_26_351783	290	4	.	.	.
10_1101-2020_10_26_351783	291	1	2011;7:275–89	2011;7:275–89	LS
10_1101-2020_10_26_351783	291	2	.	.	.
10_1101-2020_10_26_351783	292	1	2	2	LS
10_1101-2020_10_26_351783	292	2	.	.	.
10_1101-2020_10_26_351783	293	1	Santiago	Santiago	NNP
10_1101-2020_10_26_351783	293	2	JA	JA	NNP
10_1101-2020_10_26_351783	293	3	,	,	,
10_1101-2020_10_26_351783	293	4	Bottero	Bottero	NNP
10_1101-2020_10_26_351783	293	5	V	V	NNP
10_1101-2020_10_26_351783	293	6	,	,	,
10_1101-2020_10_26_351783	293	7	Potashkin	Potashkin	NNP
10_1101-2020_10_26_351783	293	8	JA	JA	NNP
10_1101-2020_10_26_351783	293	9	.	.	.
10_1101-2020_10_26_351783	294	1	Dissecting	dissect	VBG
10_1101-2020_10_26_351783	294	2	the	the	DT
10_1101-2020_10_26_351783	294	3	Molecular	Molecular	NNP
10_1101-2020_10_26_351783	294	4	Mechanisms	Mechanisms	NNPS
10_1101-2020_10_26_351783	294	5	of	of	IN
10_1101-2020_10_26_351783	294	6	Neurodegenerative	Neurodegenerative	NNP
10_1101-2020_10_26_351783	294	7	Diseases	Diseases	NNP
10_1101-2020_10_26_351783	294	8	through	through	IN
10_1101-2020_10_26_351783	294	9	Network	Network	NNP
10_1101-2020_10_26_351783	294	10	Biology	Biology	NNP
10_1101-2020_10_26_351783	294	11	.	.	.
10_1101-2020_10_26_351783	295	1	Front	front	JJ
10_1101-2020_10_26_351783	295	2	Aging	age	VBG
10_1101-2020_10_26_351783	295	3	Neurosci	Neurosci	NNP
10_1101-2020_10_26_351783	295	4	[	[	-LRB-
10_1101-2020_10_26_351783	295	5	Internet	internet	NN
10_1101-2020_10_26_351783	295	6	]	]	-RRB-
10_1101-2020_10_26_351783	295	7	.	.	.
10_1101-2020_10_26_351783	296	1	2017;9:1–13	2017;9:1–13	LS
10_1101-2020_10_26_351783	296	2	.	.	.
10_1101-2020_10_26_351783	297	1	Available	available	JJ
10_1101-2020_10_26_351783	297	2	from	from	IN
10_1101-2020_10_26_351783	297	3	:	:	:
10_1101-2020_10_26_351783	297	4	http://journal.frontiersin.org/article/10.3389/fnagi.2017.00166/full	http://journal.frontiersin.org/article/10.3389/fnagi.2017.00166/full	ADD
10_1101-2020_10_26_351783	297	5	3	3	CD
10_1101-2020_10_26_351783	297	6	.	.	.
10_1101-2020_10_26_351783	298	1	Barabási	Barabási	NNP
10_1101-2020_10_26_351783	298	2	AL	AL	NNP
10_1101-2020_10_26_351783	298	3	,	,	,
10_1101-2020_10_26_351783	298	4	Gulbahce	Gulbahce	NNP
10_1101-2020_10_26_351783	298	5	N	N	NNP
10_1101-2020_10_26_351783	298	6	,	,	,
10_1101-2020_10_26_351783	298	7	Loscalzo	Loscalzo	NNP
10_1101-2020_10_26_351783	298	8	J.	J.	NNP
10_1101-2020_10_26_351783	299	1	Network	network	NN
10_1101-2020_10_26_351783	299	2	medicine	medicine	NN
10_1101-2020_10_26_351783	299	3	:	:	:
10_1101-2020_10_26_351783	299	4	A	a	DT
10_1101-2020_10_26_351783	299	5	network	network	NN
10_1101-2020_10_26_351783	299	6	-	-	HYPH
10_1101-2020_10_26_351783	299	7	based	base	VBN
10_1101-2020_10_26_351783	299	8	approach	approach	NN
10_1101-2020_10_26_351783	299	9	to	to	IN
10_1101-2020_10_26_351783	299	10	human	human	JJ
10_1101-2020_10_26_351783	299	11	disease	disease	NN
10_1101-2020_10_26_351783	299	12	.	.	.
10_1101-2020_10_26_351783	300	1	Nat	Nat	NNP
10_1101-2020_10_26_351783	300	2	Rev	Rev	NNP
10_1101-2020_10_26_351783	300	3	Genet	Genet	NNP
10_1101-2020_10_26_351783	300	4	[	[	-LRB-
10_1101-2020_10_26_351783	300	5	Internet	internet	NN
10_1101-2020_10_26_351783	300	6	]	]	-RRB-
10_1101-2020_10_26_351783	300	7	.	.	.
10_1101-2020_10_26_351783	301	1	Nature	Nature	NNP
10_1101-2020_10_26_351783	301	2	Publishing	Publishing	NNP
10_1101-2020_10_26_351783	301	3	Group	Group	NNP
10_1101-2020_10_26_351783	301	4	;	;	:
10_1101-2020_10_26_351783	301	5	2011;12:56–68	2011;12:56–68	CD
10_1101-2020_10_26_351783	301	6	.	.	.
10_1101-2020_10_26_351783	302	1	Available	available	JJ
10_1101-2020_10_26_351783	302	2	from	from	IN
10_1101-2020_10_26_351783	302	3	:	:	:
10_1101-2020_10_26_351783	302	4	http://dx.doi.org/10.1038/nrg2918	http://dx.doi.org/10.1038/nrg2918	NNP
10_1101-2020_10_26_351783	302	5	4	4	CD
10_1101-2020_10_26_351783	302	6	.	.	.
10_1101-2020_10_26_351783	303	1	Gustafsson	Gustafsson	NNP
10_1101-2020_10_26_351783	303	2	M	M	NNP
10_1101-2020_10_26_351783	303	3	,	,	,
10_1101-2020_10_26_351783	303	4	Nestor	Nestor	NNP
10_1101-2020_10_26_351783	303	5	CE	CE	NNP
10_1101-2020_10_26_351783	303	6	,	,	,
10_1101-2020_10_26_351783	303	7	Zhang	Zhang	NNP
10_1101-2020_10_26_351783	303	8	H	H	NNP
10_1101-2020_10_26_351783	303	9	,	,	,
10_1101-2020_10_26_351783	303	10	Barabási	Barabási	NNP
10_1101-2020_10_26_351783	303	11	A	A	NNP
10_1101-2020_10_26_351783	303	12	-	-	HYPH
10_1101-2020_10_26_351783	303	13	L	L	NNP
10_1101-2020_10_26_351783	303	14	,	,	,
10_1101-2020_10_26_351783	303	15	Baranzini	Baranzini	NNP
10_1101-2020_10_26_351783	303	16	S	S	NNP
10_1101-2020_10_26_351783	303	17	,	,	,
10_1101-2020_10_26_351783	303	18	Brunak	Brunak	NNP
10_1101-2020_10_26_351783	303	19	S	S	NNP
10_1101-2020_10_26_351783	303	20	,	,	,
10_1101-2020_10_26_351783	303	21	et	et	NNP
10_1101-2020_10_26_351783	303	22	al	al	NNP
10_1101-2020_10_26_351783	303	23	.	.	.
10_1101-2020_10_26_351783	304	1	Modules	module	NNS
10_1101-2020_10_26_351783	304	2	,	,	,
10_1101-2020_10_26_351783	304	3	networks	network	NNS
10_1101-2020_10_26_351783	304	4	and	and	CC
10_1101-2020_10_26_351783	304	5	systems	system	NNS
10_1101-2020_10_26_351783	304	6	medicine	medicine	NN
10_1101-2020_10_26_351783	304	7	for	for	IN
10_1101-2020_10_26_351783	304	8	understanding	understanding	NN
10_1101-2020_10_26_351783	304	9	disease	disease	NN
10_1101-2020_10_26_351783	304	10	and	and	CC
10_1101-2020_10_26_351783	304	11	aiding	aid	VBG
10_1101-2020_10_26_351783	304	12	diagnosis	diagnosis	NN
10_1101-2020_10_26_351783	304	13	.	.	.
10_1101-2020_10_26_351783	305	1	Genome	genome	JJ
10_1101-2020_10_26_351783	305	2	Med	Med	NNP
10_1101-2020_10_26_351783	305	3	[	[	-LRB-
10_1101-2020_10_26_351783	305	4	Internet	internet	NN
10_1101-2020_10_26_351783	305	5	]	]	-RRB-
10_1101-2020_10_26_351783	305	6	.	.	.
10_1101-2020_10_26_351783	306	1	2014;6:82	2014;6:82	CD
10_1101-2020_10_26_351783	306	2	.	.	.
10_1101-2020_10_26_351783	307	1	Available	available	JJ
10_1101-2020_10_26_351783	307	2	from	from	IN
10_1101-2020_10_26_351783	307	3	:	:	:
10_1101-2020_10_26_351783	307	4	http://genomemedicine.biomedcentral.com/articles/10.1186/s13073-	http://genomemedicine.biomedcentral.com/articles/10.1186/s13073-	NNP
10_1101-2020_10_26_351783	307	5	014	014	CD
10_1101-2020_10_26_351783	307	6	-	-	HYPH
10_1101-2020_10_26_351783	307	7	0082	0082	CD
10_1101-2020_10_26_351783	307	8	-	-	HYPH
10_1101-2020_10_26_351783	307	9	6	6	CD
10_1101-2020_10_26_351783	307	10	5	5	CD
10_1101-2020_10_26_351783	307	11	.	.	.
10_1101-2020_10_26_351783	308	1	Szklarczyk	Szklarczyk	NNP
10_1101-2020_10_26_351783	308	2	D	D	NNP
10_1101-2020_10_26_351783	308	3	,	,	,
10_1101-2020_10_26_351783	308	4	Gable	Gable	NNP
10_1101-2020_10_26_351783	308	5	AL	AL	NNP
10_1101-2020_10_26_351783	308	6	,	,	,
10_1101-2020_10_26_351783	308	7	Lyon	Lyon	NNP
10_1101-2020_10_26_351783	308	8	D	D	NNP
10_1101-2020_10_26_351783	308	9	,	,	,
10_1101-2020_10_26_351783	308	10	Junge	Junge	NNP
10_1101-2020_10_26_351783	308	11	A	A	NNP
10_1101-2020_10_26_351783	308	12	,	,	,
10_1101-2020_10_26_351783	308	13	Wyder	Wyder	NNP
10_1101-2020_10_26_351783	308	14	S	S	NNP
10_1101-2020_10_26_351783	308	15	,	,	,
10_1101-2020_10_26_351783	308	16	Huerta	Huerta	NNP
10_1101-2020_10_26_351783	308	17	-	-	HYPH
10_1101-2020_10_26_351783	308	18	cepas	cepas	NNP
10_1101-2020_10_26_351783	308	19	J	J	NNP
10_1101-2020_10_26_351783	308	20	,	,	,
10_1101-2020_10_26_351783	308	21	et	et	NNP
10_1101-2020_10_26_351783	308	22	al	al	NNP
10_1101-2020_10_26_351783	308	23	.	.	.
10_1101-2020_10_26_351783	309	1	STRING	string	NN
10_1101-2020_10_26_351783	309	2	v11	v11	NN
10_1101-2020_10_26_351783	309	3	[	[	-LRB-
10_1101-2020_10_26_351783	309	4	:	:	:
10_1101-2020_10_26_351783	309	5	protein	protein	NN
10_1101-2020_10_26_351783	309	6	–	–	:
10_1101-2020_10_26_351783	309	7	protein	protein	NNP
10_1101-2020_10_26_351783	309	8	association	association	NNP
10_1101-2020_10_26_351783	309	9	networks	network	NNS
10_1101-2020_10_26_351783	309	10	with	with	IN
10_1101-2020_10_26_351783	309	11	increased	increase	VBN
10_1101-2020_10_26_351783	309	12	coverage	coverage	NN
10_1101-2020_10_26_351783	309	13	,	,	,
10_1101-2020_10_26_351783	309	14	supporting	support	VBG
10_1101-2020_10_26_351783	309	15	functional	functional	JJ
10_1101-2020_10_26_351783	309	16	discovery	discovery	NN
10_1101-2020_10_26_351783	309	17	in	in	IN
10_1101-2020_10_26_351783	309	18	genome-	genome-	NN
10_1101-2020_10_26_351783	309	19	wide	wide	JJ
10_1101-2020_10_26_351783	309	20	experimental	experimental	JJ
10_1101-2020_10_26_351783	309	21	datasets	dataset	NNS
10_1101-2020_10_26_351783	309	22	.	.	.
10_1101-2020_10_26_351783	310	1	Oxford	Oxford	NNP
10_1101-2020_10_26_351783	310	2	University	University	NNP
10_1101-2020_10_26_351783	310	3	Press	Press	NNP
10_1101-2020_10_26_351783	310	4	;	;	:
10_1101-2020_10_26_351783	310	5	2019;47:607–13	2019;47:607–13	NNP
10_1101-2020_10_26_351783	310	6	.	.	.
10_1101-2020_10_26_351783	311	1	6	6	CD
10_1101-2020_10_26_351783	311	2	.	.	.
10_1101-2020_10_26_351783	312	1	Lamparter	lamparter	NN
10_1101-2020_10_26_351783	312	2	D	D	NNP
10_1101-2020_10_26_351783	312	3	,	,	,
10_1101-2020_10_26_351783	312	4	Lin	Lin	NNP
10_1101-2020_10_26_351783	312	5	J	J	NNP
10_1101-2020_10_26_351783	312	6	,	,	,
10_1101-2020_10_26_351783	312	7	Kutalik	Kutalik	NNP
10_1101-2020_10_26_351783	312	8	Z	Z	NNP
10_1101-2020_10_26_351783	312	9	,	,	,
10_1101-2020_10_26_351783	312	10	Choobdar	Choobdar	NNP
10_1101-2020_10_26_351783	312	11	S	S	NNP
10_1101-2020_10_26_351783	312	12	,	,	,
10_1101-2020_10_26_351783	312	13	Hescott	Hescott	NNP
10_1101-2020_10_26_351783	312	14	B	B	NNP
10_1101-2020_10_26_351783	312	15	,	,	,
10_1101-2020_10_26_351783	312	16	Tomasoni	Tomasoni	NNP
10_1101-2020_10_26_351783	312	17	M	M	NNP
10_1101-2020_10_26_351783	312	18	,	,	,
10_1101-2020_10_26_351783	312	19	et	et	NNP
10_1101-2020_10_26_351783	312	20	al	al	NNP
10_1101-2020_10_26_351783	312	21	.	.	.
10_1101-2020_10_26_351783	313	1	Open	Open	NNP
10_1101-2020_10_26_351783	313	2	Community	Community	NNP
10_1101-2020_10_26_351783	313	3	Challenge	Challenge	NNP
10_1101-2020_10_26_351783	313	4	Reveals	reveal	VBZ
10_1101-2020_10_26_351783	313	5	Molecular	Molecular	NNP
10_1101-2020_10_26_351783	313	6	Network	Network	NNP
10_1101-2020_10_26_351783	313	7	Modules	Modules	NNP
10_1101-2020_10_26_351783	313	8	with	with	IN
10_1101-2020_10_26_351783	313	9	Key	Key	NNP
10_1101-2020_10_26_351783	313	10	Roles	Roles	NNPS
10_1101-2020_10_26_351783	313	11	in	in	IN
10_1101-2020_10_26_351783	313	12	Diseases	Diseases	NNP
10_1101-2020_10_26_351783	313	13	.	.	.
10_1101-2020_10_26_351783	314	1	SSRN	SSRN	NNP
10_1101-2020_10_26_351783	314	2	Electron	Electron	NNP
10_1101-2020_10_26_351783	314	3	J.	J.	NNP
10_1101-2020_10_26_351783	315	1	2018;1	2018;1	LS
10_1101-2020_10_26_351783	315	2	–	–	:
10_1101-2020_10_26_351783	315	3	63	63	CD
10_1101-2020_10_26_351783	315	4	.	.	.
10_1101-2020_10_26_351783	316	1	7	7	LS
10_1101-2020_10_26_351783	316	2	.	.	.
10_1101-2020_10_26_351783	317	1	Schadt	Schadt	NNP
10_1101-2020_10_26_351783	317	2	EE	EE	NNP
10_1101-2020_10_26_351783	317	3	.	.	.
10_1101-2020_10_26_351783	318	1	Molecular	molecular	JJ
10_1101-2020_10_26_351783	318	2	networks	network	NNS
10_1101-2020_10_26_351783	318	3	as	as	IN
10_1101-2020_10_26_351783	318	4	sensors	sensor	NNS
10_1101-2020_10_26_351783	318	5	and	and	CC
10_1101-2020_10_26_351783	318	6	drivers	driver	NNS
10_1101-2020_10_26_351783	318	7	of	of	IN
10_1101-2020_10_26_351783	318	8	common	common	JJ
10_1101-2020_10_26_351783	318	9	human	human	JJ
10_1101-2020_10_26_351783	318	10	diseases	disease	NNS
10_1101-2020_10_26_351783	318	11	.	.	.
10_1101-2020_10_26_351783	319	1	Nature	nature	NN
10_1101-2020_10_26_351783	319	2	[	[	-LRB-
10_1101-2020_10_26_351783	319	3	Internet	internet	NN
10_1101-2020_10_26_351783	319	4	]	]	-RRB-
10_1101-2020_10_26_351783	319	5	.	.	.
10_1101-2020_10_26_351783	320	1	2009;461:218–23	2009;461:218–23	CD
10_1101-2020_10_26_351783	320	2	.	.	.
10_1101-2020_10_26_351783	321	1	Available	available	JJ
10_1101-2020_10_26_351783	321	2	from	from	IN
10_1101-2020_10_26_351783	321	3	:	:	:
10_1101-2020_10_26_351783	321	4	http://www.nature.com/doifinder/10.1038/nature08454	http://www.nature.com/doifinder/10.1038/nature08454	NNP
10_1101-2020_10_26_351783	321	5	8	8	CD
10_1101-2020_10_26_351783	321	6	.	.	.
10_1101-2020_10_26_351783	322	1	Ghiassian	ghiassian	JJ
10_1101-2020_10_26_351783	322	2	SD	SD	NNP
10_1101-2020_10_26_351783	322	3	,	,	,
10_1101-2020_10_26_351783	322	4	Menche	Menche	NNP
10_1101-2020_10_26_351783	322	5	J	J	NNP
10_1101-2020_10_26_351783	322	6	,	,	,
10_1101-2020_10_26_351783	322	7	Barabási	Barabási	NNP
10_1101-2020_10_26_351783	322	8	AL	AL	NNP
10_1101-2020_10_26_351783	322	9	.	.	.
10_1101-2020_10_26_351783	323	1	A	a	DT
10_1101-2020_10_26_351783	323	2	DIseAse	disease	NN
10_1101-2020_10_26_351783	323	3	MOdule	MOdule	NNP
10_1101-2020_10_26_351783	323	4	Detection	detection	NN
10_1101-2020_10_26_351783	323	5	(	(	-LRB-
10_1101-2020_10_26_351783	323	6	DIAMOnD	diamond	NN
10_1101-2020_10_26_351783	323	7	)	)	-RRB-
10_1101-2020_10_26_351783	323	8	Algorithm	Algorithm	NNP
10_1101-2020_10_26_351783	323	9	Derived	derive	VBN
10_1101-2020_10_26_351783	323	10	from	from	IN
10_1101-2020_10_26_351783	323	11	a	a	DT
10_1101-2020_10_26_351783	323	12	Systematic	Systematic	NNP
10_1101-2020_10_26_351783	323	13	Analysis	Analysis	NNP
10_1101-2020_10_26_351783	323	14	of	of	IN
10_1101-2020_10_26_351783	323	15	Connectivity	Connectivity	NNP
10_1101-2020_10_26_351783	323	16	Patterns	Patterns	NNPS
10_1101-2020_10_26_351783	323	17	of	of	IN
10_1101-2020_10_26_351783	323	18	Disease	Disease	NNP
10_1101-2020_10_26_351783	323	19	Proteins	Proteins	NNPS
10_1101-2020_10_26_351783	323	20	in	in	IN
10_1101-2020_10_26_351783	323	21	the	the	DT
10_1101-2020_10_26_351783	323	22	Human	Human	NNP
10_1101-2020_10_26_351783	323	23	Interactome	Interactome	NNP
10_1101-2020_10_26_351783	323	24	.	.	.
10_1101-2020_10_26_351783	324	1	Rzhetsky	Rzhetsky	NNP
10_1101-2020_10_26_351783	324	2	A	A	NNP
10_1101-2020_10_26_351783	324	3	,	,	,
10_1101-2020_10_26_351783	324	4	editor	editor	NN
10_1101-2020_10_26_351783	324	5	.	.	.
10_1101-2020_10_26_351783	325	1	PLoS	PLoS	:
10_1101-2020_10_26_351783	325	2	Comput	comput	NN
10_1101-2020_10_26_351783	325	3	Biol	Biol	NNP
10_1101-2020_10_26_351783	325	4	[	[	-LRB-
10_1101-2020_10_26_351783	325	5	Internet	internet	NN
10_1101-2020_10_26_351783	325	6	]	]	-RRB-
10_1101-2020_10_26_351783	325	7	.	.	.
10_1101-2020_10_26_351783	326	1	2015;11	2015;11	CD
10_1101-2020_10_26_351783	326	2	:	:	:
10_1101-2020_10_26_351783	326	3	e1004120	e1004120	NNP
10_1101-2020_10_26_351783	326	4	.	.	.
10_1101-2020_10_26_351783	327	1	Available	available	JJ
10_1101-2020_10_26_351783	327	2	from	from	IN
10_1101-2020_10_26_351783	327	3	:	:	:
10_1101-2020_10_26_351783	327	4	(	(	-LRB-
10_1101-2020_10_26_351783	327	5	which	which	WDT
10_1101-2020_10_26_351783	327	6	was	be	VBD
10_1101-2020_10_26_351783	327	7	not	not	RB
10_1101-2020_10_26_351783	327	8	certified	certify	VBN
10_1101-2020_10_26_351783	327	9	by	by	IN
10_1101-2020_10_26_351783	327	10	peer	peer	NN
10_1101-2020_10_26_351783	327	11	review	review	NN
10_1101-2020_10_26_351783	327	12	)	)	-RRB-
10_1101-2020_10_26_351783	327	13	is	be	VBZ
10_1101-2020_10_26_351783	327	14	the	the	DT
10_1101-2020_10_26_351783	327	15	author	author	NN
10_1101-2020_10_26_351783	327	16	/	/	SYM
10_1101-2020_10_26_351783	327	17	funder	funder	NN
10_1101-2020_10_26_351783	327	18	.	.	.
10_1101-2020_10_26_351783	328	1	All	all	DT
10_1101-2020_10_26_351783	328	2	rights	right	NNS
10_1101-2020_10_26_351783	328	3	reserved	reserve	VBD
10_1101-2020_10_26_351783	328	4	.	.	.
10_1101-2020_10_26_351783	329	1	No	no	DT
10_1101-2020_10_26_351783	329	2	reuse	reuse	NN
10_1101-2020_10_26_351783	329	3	allowed	allow	VBN
10_1101-2020_10_26_351783	329	4	without	without	IN
10_1101-2020_10_26_351783	329	5	permission	permission	NN
10_1101-2020_10_26_351783	329	6	.	.	.
10_1101-2020_10_26_351783	330	1	The	the	DT
10_1101-2020_10_26_351783	330	2	copyright	copyright	NN
10_1101-2020_10_26_351783	330	3	holder	holder	NN
10_1101-2020_10_26_351783	330	4	for	for	IN
10_1101-2020_10_26_351783	330	5	this	this	DT
10_1101-2020_10_26_351783	330	6	preprintthis	preprintthis	NN
10_1101-2020_10_26_351783	330	7	version	version	NN
10_1101-2020_10_26_351783	330	8	posted	post	VBD
10_1101-2020_10_26_351783	330	9	January	January	NNP
10_1101-2020_10_26_351783	330	10	6	6	CD
10_1101-2020_10_26_351783	330	11	,	,	,
10_1101-2020_10_26_351783	330	12	2021	2021	CD
10_1101-2020_10_26_351783	330	13	.	.	.
10_1101-2020_10_26_351783	330	14	;	;	:
10_1101-2020_10_26_351783	330	15	https://doi.org/10.1101/2020.10.26.351783doi	https://doi.org/10.1101/2020.10.26.351783doi	ADD
10_1101-2020_10_26_351783	330	16	:	:	:
10_1101-2020_10_26_351783	330	17	bioRxiv	biorxiv	VB
10_1101-2020_10_26_351783	330	18	preprint	preprint	NN
10_1101-2020_10_26_351783	330	19	https://doi.org/10.1101/2020.10.26.351783	https://doi.org/10.1101/2020.10.26.351783	NNP
10_1101-2020_10_26_351783	330	20	21	21	CD
10_1101-2020_10_26_351783	330	21	https://dx.plos.org/10.1371/journal.pcbi.1004120	https://dx.plos.org/10.1371/journal.pcbi.1004120	CD
10_1101-2020_10_26_351783	330	22	9	9	CD
10_1101-2020_10_26_351783	330	23	.	.	.
10_1101-2020_10_26_351783	331	1	Hellberg	Hellberg	NNP
10_1101-2020_10_26_351783	331	2	S	S	NNP
10_1101-2020_10_26_351783	331	3	,	,	,
10_1101-2020_10_26_351783	331	4	Eklund	Eklund	NNP
10_1101-2020_10_26_351783	331	5	D	D	NNP
10_1101-2020_10_26_351783	331	6	,	,	,
10_1101-2020_10_26_351783	331	7	Gawel	Gawel	NNP
10_1101-2020_10_26_351783	331	8	DR	DR	NNP
10_1101-2020_10_26_351783	331	9	,	,	,
10_1101-2020_10_26_351783	331	10	Köpsén	Köpsén	NNP
10_1101-2020_10_26_351783	331	11	M	M	NNP
10_1101-2020_10_26_351783	331	12	,	,	,
10_1101-2020_10_26_351783	331	13	Zhang	Zhang	NNP
10_1101-2020_10_26_351783	331	14	H	H	NNP
10_1101-2020_10_26_351783	331	15	,	,	,
10_1101-2020_10_26_351783	331	16	Nestor	Nestor	NNP
10_1101-2020_10_26_351783	331	17	CE	CE	NNP
10_1101-2020_10_26_351783	331	18	,	,	,
10_1101-2020_10_26_351783	331	19	et	et	NNP
10_1101-2020_10_26_351783	331	20	al	al	NNP
10_1101-2020_10_26_351783	331	21	.	.	.
10_1101-2020_10_26_351783	332	1	Dynamic	Dynamic	NNP
10_1101-2020_10_26_351783	332	2	Response	Response	NNP
10_1101-2020_10_26_351783	332	3	Genes	Genes	NNPS
10_1101-2020_10_26_351783	332	4	in	in	IN
10_1101-2020_10_26_351783	332	5	CD4	CD4	NNP
10_1101-2020_10_26_351783	332	6	+	+	SYM
10_1101-2020_10_26_351783	332	7	T	t	NN
10_1101-2020_10_26_351783	332	8	Cells	cell	NNS
10_1101-2020_10_26_351783	332	9	Reveal	reveal	VBP
10_1101-2020_10_26_351783	332	10	a	a	DT
10_1101-2020_10_26_351783	332	11	Network	Network	NNP
10_1101-2020_10_26_351783	332	12	of	of	IN
10_1101-2020_10_26_351783	332	13	Interactive	Interactive	NNP
10_1101-2020_10_26_351783	332	14	Proteins	Proteins	NNPS
10_1101-2020_10_26_351783	332	15	that	that	IN
10_1101-2020_10_26_351783	332	16	Classifies	Classifies	NNPS
10_1101-2020_10_26_351783	332	17	Disease	Disease	NNP
10_1101-2020_10_26_351783	332	18	Activity	Activity	NNP
10_1101-2020_10_26_351783	332	19	in	in	IN
10_1101-2020_10_26_351783	332	20	Multiple	Multiple	NNP
10_1101-2020_10_26_351783	332	21	Sclerosis	Sclerosis	NNP
10_1101-2020_10_26_351783	332	22	.	.	.
10_1101-2020_10_26_351783	333	1	Cell	Cell	NNP
10_1101-2020_10_26_351783	333	2	Rep.	Rep.	NNP
10_1101-2020_10_26_351783	333	3	2016;16:2928–39	2016;16:2928–39	CD
10_1101-2020_10_26_351783	333	4	.	.	.
10_1101-2020_10_26_351783	334	1	10	10	CD
10_1101-2020_10_26_351783	334	2	.	.	.
10_1101-2020_10_26_351783	335	1	Wang	Wang	NNP
10_1101-2020_10_26_351783	335	2	H	H	NNP
10_1101-2020_10_26_351783	335	3	,	,	,
10_1101-2020_10_26_351783	335	4	Rogers	Rogers	NNP
10_1101-2020_10_26_351783	335	5	G	G	NNP
10_1101-2020_10_26_351783	335	6	,	,	,
10_1101-2020_10_26_351783	335	7	Benson	Benson	NNP
10_1101-2020_10_26_351783	335	8	M	M	NNP
10_1101-2020_10_26_351783	335	9	,	,	,
10_1101-2020_10_26_351783	335	10	Jarvelin	Jarvelin	NNP
10_1101-2020_10_26_351783	335	11	M	M	NNP
10_1101-2020_10_26_351783	335	12	-	-	HYPH
10_1101-2020_10_26_351783	335	13	R	R	NNP
10_1101-2020_10_26_351783	335	14	,	,	,
10_1101-2020_10_26_351783	335	15	Chavali	Chavali	NNP
10_1101-2020_10_26_351783	335	16	S	S	NNP
10_1101-2020_10_26_351783	335	17	,	,	,
10_1101-2020_10_26_351783	335	18	Ramasamy	Ramasamy	NNP
10_1101-2020_10_26_351783	335	19	A	A	NNP
10_1101-2020_10_26_351783	335	20	,	,	,
10_1101-2020_10_26_351783	335	21	et	et	NNP
10_1101-2020_10_26_351783	335	22	al	al	NNP
10_1101-2020_10_26_351783	335	23	.	.	.
10_1101-2020_10_26_351783	336	1	Highly	highly	RB
10_1101-2020_10_26_351783	336	2	interconnected	interconnect	VBN
10_1101-2020_10_26_351783	336	3	genes	gene	NNS
10_1101-2020_10_26_351783	336	4	in	in	IN
10_1101-2020_10_26_351783	336	5	disease	disease	NN
10_1101-2020_10_26_351783	336	6	-	-	HYPH
10_1101-2020_10_26_351783	336	7	specific	specific	JJ
10_1101-2020_10_26_351783	336	8	networks	network	NNS
10_1101-2020_10_26_351783	336	9	are	be	VBP
10_1101-2020_10_26_351783	336	10	enriched	enrich	VBN
10_1101-2020_10_26_351783	336	11	for	for	IN
10_1101-2020_10_26_351783	336	12	disease	disease	NN
10_1101-2020_10_26_351783	336	13	-	-	HYPH
10_1101-2020_10_26_351783	336	14	associated	associate	VBN
10_1101-2020_10_26_351783	336	15	polymorphisms	polymorphism	NNS
10_1101-2020_10_26_351783	336	16	.	.	.
10_1101-2020_10_26_351783	337	1	Genome	Genome	NNP
10_1101-2020_10_26_351783	337	2	Biol	Biol	NNP
10_1101-2020_10_26_351783	337	3	.	.	.
10_1101-2020_10_26_351783	338	1	2012;13	2012;13	CD
10_1101-2020_10_26_351783	338	2	:	:	:
10_1101-2020_10_26_351783	338	3	R46	R46	NNP
10_1101-2020_10_26_351783	338	4	.	.	.
10_1101-2020_10_26_351783	339	1	11	11	CD
10_1101-2020_10_26_351783	339	2	.	.	.
10_1101-2020_10_26_351783	340	1	Langfelder	Langfelder	NNP
10_1101-2020_10_26_351783	340	2	P	P	NNP
10_1101-2020_10_26_351783	340	3	,	,	,
10_1101-2020_10_26_351783	340	4	Horvath	Horvath	NNP
10_1101-2020_10_26_351783	340	5	S.	S.	NNP
10_1101-2020_10_26_351783	340	6	WGCNA	WGCNA	NNP
10_1101-2020_10_26_351783	340	7	:	:	:
10_1101-2020_10_26_351783	340	8	An	an	DT
10_1101-2020_10_26_351783	340	9	R	r	NN
10_1101-2020_10_26_351783	340	10	package	package	NN
10_1101-2020_10_26_351783	340	11	for	for	IN
10_1101-2020_10_26_351783	340	12	weighted	weighted	JJ
10_1101-2020_10_26_351783	340	13	correlation	correlation	NN
10_1101-2020_10_26_351783	340	14	network	network	NN
10_1101-2020_10_26_351783	340	15	analysis	analysis	NN
10_1101-2020_10_26_351783	340	16	.	.	.
10_1101-2020_10_26_351783	341	1	BMC	BMC	NNP
10_1101-2020_10_26_351783	341	2	Bioinformatics	Bioinformatics	NNP
10_1101-2020_10_26_351783	341	3	.	.	.
10_1101-2020_10_26_351783	342	1	2008;9	2008;9	LS
10_1101-2020_10_26_351783	342	2	.	.	.
10_1101-2020_10_26_351783	343	1	12	12	CD
10_1101-2020_10_26_351783	343	2	.	.	.
10_1101-2020_10_26_351783	344	1	Choobdar	Choobdar	NNP
10_1101-2020_10_26_351783	344	2	S	S	NNP
10_1101-2020_10_26_351783	344	3	,	,	,
10_1101-2020_10_26_351783	344	4	Ahsen	Ahsen	NNP
10_1101-2020_10_26_351783	344	5	ME	ME	NNP
10_1101-2020_10_26_351783	344	6	,	,	,
10_1101-2020_10_26_351783	344	7	Crawford	Crawford	NNP
10_1101-2020_10_26_351783	344	8	J	J	NNP
10_1101-2020_10_26_351783	344	9	,	,	,
10_1101-2020_10_26_351783	344	10	Tomasoni	Tomasoni	NNP
10_1101-2020_10_26_351783	344	11	M	M	NNP
10_1101-2020_10_26_351783	344	12	,	,	,
10_1101-2020_10_26_351783	344	13	Fang	Fang	NNP
10_1101-2020_10_26_351783	344	14	T	T	NNP
10_1101-2020_10_26_351783	344	15	,	,	,
10_1101-2020_10_26_351783	344	16	Lamparter	Lamparter	NNP
10_1101-2020_10_26_351783	344	17	D	D	NNP
10_1101-2020_10_26_351783	344	18	,	,	,
10_1101-2020_10_26_351783	344	19	et	et	NNP
10_1101-2020_10_26_351783	344	20	al	al	NNP
10_1101-2020_10_26_351783	344	21	.	.	.
10_1101-2020_10_26_351783	345	1	Assessment	assessment	NN
10_1101-2020_10_26_351783	345	2	of	of	IN
10_1101-2020_10_26_351783	345	3	network	network	NN
10_1101-2020_10_26_351783	345	4	module	module	JJ
10_1101-2020_10_26_351783	345	5	identification	identification	NN
10_1101-2020_10_26_351783	345	6	across	across	IN
10_1101-2020_10_26_351783	345	7	complex	complex	JJ
10_1101-2020_10_26_351783	345	8	diseases	disease	NNS
10_1101-2020_10_26_351783	345	9	.	.	.
10_1101-2020_10_26_351783	346	1	Nat	Nat	NNP
10_1101-2020_10_26_351783	346	2	Methods	Methods	NNP
10_1101-2020_10_26_351783	346	3	.	.	.
10_1101-2020_10_26_351783	347	1	2019;16:843–52	2019;16:843–52	NNP
10_1101-2020_10_26_351783	347	2	.	.	.
10_1101-2020_10_26_351783	348	1	13	13	CD
10_1101-2020_10_26_351783	348	2	.	.	.
10_1101-2020_10_26_351783	348	3	de	de	NNP
10_1101-2020_10_26_351783	348	4	Weerd	Weerd	NNP
10_1101-2020_10_26_351783	348	5	HA	HA	NNP
10_1101-2020_10_26_351783	348	6	,	,	,
10_1101-2020_10_26_351783	348	7	Badam	Badam	NNP
10_1101-2020_10_26_351783	348	8	TVS	TVS	NNP
10_1101-2020_10_26_351783	348	9	,	,	,
10_1101-2020_10_26_351783	348	10	Martínez	Martínez	NNP
10_1101-2020_10_26_351783	348	11	-	-	HYPH
10_1101-2020_10_26_351783	348	12	Enguita	Enguita	NNP
10_1101-2020_10_26_351783	348	13	D	D	NNP
10_1101-2020_10_26_351783	348	14	,	,	,
10_1101-2020_10_26_351783	348	15	Åkesson	Åkesson	NNP
10_1101-2020_10_26_351783	348	16	J	J	NNP
10_1101-2020_10_26_351783	348	17	,	,	,
10_1101-2020_10_26_351783	348	18	Muthas	Muthas	NNP
10_1101-2020_10_26_351783	348	19	D	D	NNP
10_1101-2020_10_26_351783	348	20	,	,	,
10_1101-2020_10_26_351783	348	21	Gustafsson	Gustafsson	NNP
10_1101-2020_10_26_351783	348	22	M	M	NNP
10_1101-2020_10_26_351783	348	23	,	,	,
10_1101-2020_10_26_351783	348	24	et	et	NNP
10_1101-2020_10_26_351783	348	25	al	al	NNP
10_1101-2020_10_26_351783	348	26	.	.	.
10_1101-2020_10_26_351783	349	1	MODifieR	MODifieR	NNP
10_1101-2020_10_26_351783	349	2	:	:	:
10_1101-2020_10_26_351783	349	3	an	an	DT
10_1101-2020_10_26_351783	349	4	Ensemble	Ensemble	NNP
10_1101-2020_10_26_351783	349	5	R	r	NN
10_1101-2020_10_26_351783	349	6	Package	package	NN
10_1101-2020_10_26_351783	349	7	for	for	IN
10_1101-2020_10_26_351783	349	8	Inference	Inference	NNP
10_1101-2020_10_26_351783	349	9	of	of	IN
10_1101-2020_10_26_351783	349	10	Disease	Disease	NNP
10_1101-2020_10_26_351783	349	11	Modules	Modules	NNP
10_1101-2020_10_26_351783	349	12	from	from	IN
10_1101-2020_10_26_351783	349	13	Transcriptomics	Transcriptomics	NNP
10_1101-2020_10_26_351783	349	14	Networks	Networks	NNP
10_1101-2020_10_26_351783	349	15	.	.	.
10_1101-2020_10_26_351783	350	1	Bioinformatics	bioinformatic	NNS
10_1101-2020_10_26_351783	350	2	.	.	.
10_1101-2020_10_26_351783	351	1	2020;1–2	2020;1–2	LS
10_1101-2020_10_26_351783	351	2	.	.	.
10_1101-2020_10_26_351783	352	1	14	14	CD
10_1101-2020_10_26_351783	352	2	.	.	.
10_1101-2020_10_26_351783	353	1	Tian	Tian	NNP
10_1101-2020_10_26_351783	353	2	Y	Y	NNP
10_1101-2020_10_26_351783	353	3	,	,	,
10_1101-2020_10_26_351783	353	4	Morris	Morris	NNP
10_1101-2020_10_26_351783	353	5	TJ	TJ	NNP
10_1101-2020_10_26_351783	353	6	,	,	,
10_1101-2020_10_26_351783	353	7	Webster	Webster	NNP
10_1101-2020_10_26_351783	353	8	AP	AP	NNP
10_1101-2020_10_26_351783	353	9	,	,	,
10_1101-2020_10_26_351783	353	10	Yang	Yang	NNP
10_1101-2020_10_26_351783	353	11	Z	Z	NNP
10_1101-2020_10_26_351783	353	12	,	,	,
10_1101-2020_10_26_351783	353	13	Beck	Beck	NNP
10_1101-2020_10_26_351783	353	14	S	S	NNP
10_1101-2020_10_26_351783	353	15	,	,	,
10_1101-2020_10_26_351783	353	16	Feber	Feber	NNP
10_1101-2020_10_26_351783	353	17	A	A	NNP
10_1101-2020_10_26_351783	353	18	,	,	,
10_1101-2020_10_26_351783	353	19	et	et	NNP
10_1101-2020_10_26_351783	353	20	al	al	NNP
10_1101-2020_10_26_351783	353	21	.	.	.
10_1101-2020_10_26_351783	354	1	Genome	genome	JJ
10_1101-2020_10_26_351783	354	2	analysis	analysis	NN
10_1101-2020_10_26_351783	354	3	ChAMP	champ	NN
10_1101-2020_10_26_351783	354	4	[	[	-LRB-
10_1101-2020_10_26_351783	354	5	:	:	:
10_1101-2020_10_26_351783	354	6	updated	update	VBN
10_1101-2020_10_26_351783	354	7	methylation	methylation	NN
10_1101-2020_10_26_351783	354	8	analysis	analysis	NN
10_1101-2020_10_26_351783	354	9	pipeline	pipeline	NN
10_1101-2020_10_26_351783	354	10	for	for	IN
10_1101-2020_10_26_351783	354	11	Illumina	Illumina	NNP
10_1101-2020_10_26_351783	354	12	BeadChips	BeadChips	NNP
10_1101-2020_10_26_351783	354	13	.	.	.
10_1101-2020_10_26_351783	355	1	2017;33:3982–4	2017;33:3982–4	CD
10_1101-2020_10_26_351783	355	2	.	.	.
10_1101-2020_10_26_351783	356	1	15	15	CD
10_1101-2020_10_26_351783	356	2	.	.	.
10_1101-2020_10_26_351783	357	1	Teschendorff	Teschendorff	NNP
10_1101-2020_10_26_351783	357	2	AE	AE	NNP
10_1101-2020_10_26_351783	357	3	,	,	,
10_1101-2020_10_26_351783	357	4	Marabita	Marabita	NNP
10_1101-2020_10_26_351783	357	5	F	F	NNP
10_1101-2020_10_26_351783	357	6	,	,	,
10_1101-2020_10_26_351783	357	7	Lechner	Lechner	NNP
10_1101-2020_10_26_351783	357	8	M	M	NNP
10_1101-2020_10_26_351783	357	9	,	,	,
10_1101-2020_10_26_351783	357	10	Bartlett	Bartlett	NNP
10_1101-2020_10_26_351783	357	11	T	T	NNP
10_1101-2020_10_26_351783	357	12	,	,	,
10_1101-2020_10_26_351783	357	13	Tegner	Tegner	NNP
10_1101-2020_10_26_351783	357	14	J	J	NNP
10_1101-2020_10_26_351783	357	15	,	,	,
10_1101-2020_10_26_351783	357	16	Gomez	Gomez	NNP
10_1101-2020_10_26_351783	357	17	-	-	HYPH
10_1101-2020_10_26_351783	357	18	cabrero	cabrero	NNP
10_1101-2020_10_26_351783	357	19	D	D	NNP
10_1101-2020_10_26_351783	357	20	,	,	,
10_1101-2020_10_26_351783	357	21	et	et	NNP
10_1101-2020_10_26_351783	357	22	al	al	NNP
10_1101-2020_10_26_351783	357	23	.	.	.
10_1101-2020_10_26_351783	358	1	Gene	gene	NN
10_1101-2020_10_26_351783	358	2	expression	expression	NN
10_1101-2020_10_26_351783	358	3	A	a	DT
10_1101-2020_10_26_351783	358	4	beta	beta	NN
10_1101-2020_10_26_351783	358	5	-	-	HYPH
10_1101-2020_10_26_351783	358	6	mixture	mixture	NN
10_1101-2020_10_26_351783	358	7	quantile	quantile	NN
10_1101-2020_10_26_351783	358	8	normalization	normalization	NN
10_1101-2020_10_26_351783	358	9	method	method	NN
10_1101-2020_10_26_351783	358	10	for	for	IN
10_1101-2020_10_26_351783	358	11	correcting	correct	VBG
10_1101-2020_10_26_351783	358	12	probe	probe	NN
10_1101-2020_10_26_351783	358	13	design	design	NN
10_1101-2020_10_26_351783	358	14	bias	bias	NN
10_1101-2020_10_26_351783	358	15	in	in	IN
10_1101-2020_10_26_351783	358	16	Illumina	Illumina	NNP
10_1101-2020_10_26_351783	358	17	Infinium	Infinium	NNP
10_1101-2020_10_26_351783	358	18	450	450	CD
10_1101-2020_10_26_351783	358	19	k	k	NN
10_1101-2020_10_26_351783	358	20	DNA	dna	NN
10_1101-2020_10_26_351783	358	21	methylation	methylation	NN
10_1101-2020_10_26_351783	358	22	data	datum	NNS
10_1101-2020_10_26_351783	358	23	.	.	.
10_1101-2020_10_26_351783	359	1	2013;29:189–96	2013;29:189–96	CD
10_1101-2020_10_26_351783	359	2	.	.	.
10_1101-2020_10_26_351783	360	1	16	16	CD
10_1101-2020_10_26_351783	360	2	.	.	.
10_1101-2020_10_26_351783	361	1	Johnson	Johnson	NNP
10_1101-2020_10_26_351783	361	2	WE	WE	NNP
10_1101-2020_10_26_351783	361	3	,	,	,
10_1101-2020_10_26_351783	361	4	Li	Li	NNP
10_1101-2020_10_26_351783	361	5	C.	C.	NNP
10_1101-2020_10_26_351783	361	6	Adjusting	Adjusting	NNP
10_1101-2020_10_26_351783	361	7	batch	batch	NN
10_1101-2020_10_26_351783	361	8	effects	effect	NNS
10_1101-2020_10_26_351783	361	9	in	in	IN
10_1101-2020_10_26_351783	361	10	microarray	microarray	JJ
10_1101-2020_10_26_351783	361	11	expression	expression	NN
10_1101-2020_10_26_351783	361	12	data	datum	NNS
10_1101-2020_10_26_351783	361	13	using	use	VBG
10_1101-2020_10_26_351783	361	14	empirical	empirical	JJ
10_1101-2020_10_26_351783	361	15	Bayes	Bayes	NNP
10_1101-2020_10_26_351783	361	16	methods	method	NNS
10_1101-2020_10_26_351783	361	17	.	.	.
10_1101-2020_10_26_351783	362	1	2007;118–27	2007;118–27	NN
10_1101-2020_10_26_351783	362	2	.	.	.
10_1101-2020_10_26_351783	363	1	17	17	CD
10_1101-2020_10_26_351783	363	2	.	.	.
10_1101-2020_10_26_351783	364	1	Ritchie	Ritchie	NNP
10_1101-2020_10_26_351783	364	2	ME	ME	NNP
10_1101-2020_10_26_351783	364	3	,	,	,
10_1101-2020_10_26_351783	364	4	Phipson	Phipson	NNP
10_1101-2020_10_26_351783	364	5	B	B	NNP
10_1101-2020_10_26_351783	364	6	,	,	,
10_1101-2020_10_26_351783	364	7	Wu	Wu	NNP
10_1101-2020_10_26_351783	364	8	D	D	NNP
10_1101-2020_10_26_351783	364	9	,	,	,
10_1101-2020_10_26_351783	364	10	Hu	Hu	NNP
10_1101-2020_10_26_351783	364	11	Y	Y	NNP
10_1101-2020_10_26_351783	364	12	,	,	,
10_1101-2020_10_26_351783	364	13	Law	Law	NNP
10_1101-2020_10_26_351783	364	14	CW	CW	NNP
10_1101-2020_10_26_351783	364	15	,	,	,
10_1101-2020_10_26_351783	364	16	Shi	Shi	NNP
10_1101-2020_10_26_351783	364	17	W	W	NNP
10_1101-2020_10_26_351783	364	18	,	,	,
10_1101-2020_10_26_351783	364	19	et	et	NNP
10_1101-2020_10_26_351783	364	20	al	al	NNP
10_1101-2020_10_26_351783	364	21	.	.	.
10_1101-2020_10_26_351783	365	1	limma	limma	NNP
10_1101-2020_10_26_351783	365	2	powers	powers	NNP
10_1101-2020_10_26_351783	365	3	differential	differential	VBP
10_1101-2020_10_26_351783	365	4	expression	expression	NN
10_1101-2020_10_26_351783	365	5	analyses	analysis	NNS
10_1101-2020_10_26_351783	365	6	for	for	IN
10_1101-2020_10_26_351783	365	7	RNA	RNA	NNP
10_1101-2020_10_26_351783	365	8	-	-	HYPH
10_1101-2020_10_26_351783	365	9	sequencing	sequencing	NN
10_1101-2020_10_26_351783	365	10	and	and	CC
10_1101-2020_10_26_351783	365	11	microarray	microarray	JJ
10_1101-2020_10_26_351783	365	12	studies	study	NNS
10_1101-2020_10_26_351783	365	13	.	.	.
10_1101-2020_10_26_351783	366	1	2015;43	2015;43	NNP
10_1101-2020_10_26_351783	366	2	.	.	.
10_1101-2020_10_26_351783	367	1	(	(	-LRB-
10_1101-2020_10_26_351783	367	2	which	which	WDT
10_1101-2020_10_26_351783	367	3	was	be	VBD
10_1101-2020_10_26_351783	367	4	not	not	RB
10_1101-2020_10_26_351783	367	5	certified	certify	VBN
10_1101-2020_10_26_351783	367	6	by	by	IN
10_1101-2020_10_26_351783	367	7	peer	peer	NN
10_1101-2020_10_26_351783	367	8	review	review	NN
10_1101-2020_10_26_351783	367	9	)	)	-RRB-
10_1101-2020_10_26_351783	367	10	is	be	VBZ
10_1101-2020_10_26_351783	367	11	the	the	DT
10_1101-2020_10_26_351783	367	12	author	author	NN
10_1101-2020_10_26_351783	367	13	/	/	SYM
10_1101-2020_10_26_351783	367	14	funder	funder	NN
10_1101-2020_10_26_351783	367	15	.	.	.
10_1101-2020_10_26_351783	368	1	All	all	DT
10_1101-2020_10_26_351783	368	2	rights	right	NNS
10_1101-2020_10_26_351783	368	3	reserved	reserve	VBD
10_1101-2020_10_26_351783	368	4	.	.	.
10_1101-2020_10_26_351783	369	1	No	no	DT
10_1101-2020_10_26_351783	369	2	reuse	reuse	NN
10_1101-2020_10_26_351783	369	3	allowed	allow	VBN
10_1101-2020_10_26_351783	369	4	without	without	IN
10_1101-2020_10_26_351783	369	5	permission	permission	NN
10_1101-2020_10_26_351783	369	6	.	.	.
10_1101-2020_10_26_351783	370	1	The	the	DT
10_1101-2020_10_26_351783	370	2	copyright	copyright	NN
10_1101-2020_10_26_351783	370	3	holder	holder	NN
10_1101-2020_10_26_351783	370	4	for	for	IN
10_1101-2020_10_26_351783	370	5	this	this	DT
10_1101-2020_10_26_351783	370	6	preprintthis	preprintthis	NN
10_1101-2020_10_26_351783	370	7	version	version	NN
10_1101-2020_10_26_351783	370	8	posted	post	VBD
10_1101-2020_10_26_351783	370	9	January	January	NNP
10_1101-2020_10_26_351783	370	10	6	6	CD
10_1101-2020_10_26_351783	370	11	,	,	,
10_1101-2020_10_26_351783	370	12	2021	2021	CD
10_1101-2020_10_26_351783	370	13	.	.	.
10_1101-2020_10_26_351783	370	14	;	;	:
10_1101-2020_10_26_351783	370	15	https://doi.org/10.1101/2020.10.26.351783doi	https://doi.org/10.1101/2020.10.26.351783doi	ADD
10_1101-2020_10_26_351783	370	16	:	:	:
10_1101-2020_10_26_351783	370	17	bioRxiv	biorxiv	IN
10_1101-2020_10_26_351783	370	18	preprint	preprint	NN
10_1101-2020_10_26_351783	370	19	https://doi.org/10.1101/2020.10.26.351783	https://doi.org/10.1101/2020.10.26.351783	NNP
10_1101-2020_10_26_351783	370	20	22	22	CD
10_1101-2020_10_26_351783	370	21	18	18	CD
10_1101-2020_10_26_351783	370	22	.	.	.
10_1101-2020_10_26_351783	371	1	Lamparter	lamparter	NN
10_1101-2020_10_26_351783	371	2	D	d	NN
10_1101-2020_10_26_351783	371	3	,	,	,
10_1101-2020_10_26_351783	371	4	Marbach	Marbach	NNP
10_1101-2020_10_26_351783	371	5	D	D	NNP
10_1101-2020_10_26_351783	371	6	,	,	,
10_1101-2020_10_26_351783	371	7	Rueedi	Rueedi	NNP
10_1101-2020_10_26_351783	371	8	R	R	NNP
10_1101-2020_10_26_351783	371	9	,	,	,
10_1101-2020_10_26_351783	371	10	Kutalik	Kutalik	NNP
10_1101-2020_10_26_351783	371	11	Z	Z	NNP
10_1101-2020_10_26_351783	371	12	,	,	,
10_1101-2020_10_26_351783	371	13	Bergmann	Bergmann	NNP
10_1101-2020_10_26_351783	371	14	S.	S.	NNP
10_1101-2020_10_26_351783	371	15	Fast	Fast	NNP
10_1101-2020_10_26_351783	371	16	and	and	CC
10_1101-2020_10_26_351783	371	17	Rigorous	rigorous	JJ
10_1101-2020_10_26_351783	371	18	Computation	Computation	NNP
10_1101-2020_10_26_351783	371	19	of	of	IN
10_1101-2020_10_26_351783	371	20	Gene	Gene	NNP
10_1101-2020_10_26_351783	371	21	and	and	CC
10_1101-2020_10_26_351783	371	22	Pathway	Pathway	NNP
10_1101-2020_10_26_351783	371	23	Scores	Scores	NNPS
10_1101-2020_10_26_351783	371	24	from	from	IN
10_1101-2020_10_26_351783	371	25	SNP	SNP	NNP
10_1101-2020_10_26_351783	371	26	-	-	HYPH
10_1101-2020_10_26_351783	371	27	Based	Based	NNP
10_1101-2020_10_26_351783	371	28	Summary	Summary	NNP
10_1101-2020_10_26_351783	371	29	Statistics	Statistics	NNPS
10_1101-2020_10_26_351783	371	30	.	.	.
10_1101-2020_10_26_351783	372	1	PLoS	PLoS	:
10_1101-2020_10_26_351783	372	2	Comput	Comput	NNP
10_1101-2020_10_26_351783	372	3	Biol	Biol	NNP
10_1101-2020_10_26_351783	372	4	.	.	.
10_1101-2020_10_26_351783	373	1	2016;12:1–20	2016;12:1–20	LS
10_1101-2020_10_26_351783	373	2	.	.	.
10_1101-2020_10_26_351783	374	1	19	19	CD
10_1101-2020_10_26_351783	374	2	.	.	.
10_1101-2020_10_26_351783	375	1	Mosteller	Mosteller	NNP
10_1101-2020_10_26_351783	375	2	,	,	,
10_1101-2020_10_26_351783	375	3	F.	F.	NNP
10_1101-2020_10_26_351783	375	4	and	and	CC
10_1101-2020_10_26_351783	375	5	Fisher	Fisher	NNP
10_1101-2020_10_26_351783	375	6	R.	R.	NNP
10_1101-2020_10_26_351783	375	7	A.	A.	NNP
10_1101-2020_10_26_351783	376	1	Questions	question	NNS
10_1101-2020_10_26_351783	376	2	and	and	CC
10_1101-2020_10_26_351783	376	3	Answers	answer	NNS
10_1101-2020_10_26_351783	376	4	#	#	$
10_1101-2020_10_26_351783	376	5	14	14	CD
10_1101-2020_10_26_351783	376	6	Author	author	NN
10_1101-2020_10_26_351783	376	7	(	(	-LRB-
10_1101-2020_10_26_351783	376	8	s	s	NN
10_1101-2020_10_26_351783	376	9	):	):	NN
10_1101-2020_10_26_351783	376	10	Frederick	Frederick	NNP
10_1101-2020_10_26_351783	376	11	Mosteller	Mosteller	NNP
10_1101-2020_10_26_351783	376	12	and	and	CC
10_1101-2020_10_26_351783	376	13	R	r	NN
10_1101-2020_10_26_351783	376	14	.	.	.
10_1101-2020_10_26_351783	377	1	A	a	DT
10_1101-2020_10_26_351783	377	2	.	.	.
10_1101-2020_10_26_351783	378	1	Fisher	Fisher	NNP
10_1101-2020_10_26_351783	378	2	Published	publish	VBN
10_1101-2020_10_26_351783	378	3	by	by	IN
10_1101-2020_10_26_351783	378	4	[	[	-LRB-
10_1101-2020_10_26_351783	378	5	:	:	:
10_1101-2020_10_26_351783	378	6	Taylor	Taylor	NNP
10_1101-2020_10_26_351783	378	7	&	&	CC
10_1101-2020_10_26_351783	378	8	Francis	Francis	NNP
10_1101-2020_10_26_351783	378	9	,	,	,
10_1101-2020_10_26_351783	378	10	Ltd	Ltd	NNP
10_1101-2020_10_26_351783	378	11	.	.	.
10_1101-2020_10_26_351783	379	1	on	on	IN
10_1101-2020_10_26_351783	379	2	behalf	behalf	NN
10_1101-2020_10_26_351783	379	3	of	of	IN
10_1101-2020_10_26_351783	379	4	the	the	DT
10_1101-2020_10_26_351783	379	5	American	American	NNP
10_1101-2020_10_26_351783	379	6	Statistical	Statistical	NNP
10_1101-2020_10_26_351783	379	7	Association	Association	NNP
10_1101-2020_10_26_351783	379	8	Stable	Stable	NNP
10_1101-2020_10_26_351783	379	9	URL	URL	NNP
10_1101-2020_10_26_351783	379	10	[	[	-LRB-
10_1101-2020_10_26_351783	379	11	:	:	:
10_1101-2020_10_26_351783	379	12	http://www.jstor.org/stable/2681650	http://www.jstor.org/stable/2681650	VBG
10_1101-2020_10_26_351783	379	13	All	all	DT
10_1101-2020_10_26_351783	379	14	use	use	NN
10_1101-2020_10_26_351783	379	15	subject	subject	JJ
10_1101-2020_10_26_351783	379	16	to	to	IN
10_1101-2020_10_26_351783	379	17	http://about.jsto	http://about.jsto	NNP
10_1101-2020_10_26_351783	379	18	.	.	.
10_1101-2020_10_26_351783	380	1	1948;2:30–1	1948;2:30–1	CD
10_1101-2020_10_26_351783	380	2	.	.	.
10_1101-2020_10_26_351783	381	1	Available	available	JJ
10_1101-2020_10_26_351783	381	2	from	from	IN
10_1101-2020_10_26_351783	381	3	:	:	:
10_1101-2020_10_26_351783	381	4	http://www.jstor.org/stable/2681650	http://www.jstor.org/stable/2681650	VBG
10_1101-2020_10_26_351783	381	5	20	20	CD
10_1101-2020_10_26_351783	381	6	.	.	.
10_1101-2020_10_26_351783	382	1	Piñero	Piñero	NNP
10_1101-2020_10_26_351783	382	2	J	J	NNP
10_1101-2020_10_26_351783	382	3	,	,	,
10_1101-2020_10_26_351783	382	4	Ramírez	Ramírez	NNP
10_1101-2020_10_26_351783	382	5	-	-	HYPH
10_1101-2020_10_26_351783	382	6	Anguita	Anguita	NNP
10_1101-2020_10_26_351783	382	7	JM	JM	NNP
10_1101-2020_10_26_351783	382	8	,	,	,
10_1101-2020_10_26_351783	382	9	Saüch	Saüch	NNP
10_1101-2020_10_26_351783	382	10	-	-	HYPH
10_1101-2020_10_26_351783	382	11	Pitarch	Pitarch	NNP
10_1101-2020_10_26_351783	382	12	J	J	NNP
10_1101-2020_10_26_351783	382	13	,	,	,
10_1101-2020_10_26_351783	382	14	Ronzano	Ronzano	NNP
10_1101-2020_10_26_351783	382	15	F	F	NNP
10_1101-2020_10_26_351783	382	16	,	,	,
10_1101-2020_10_26_351783	382	17	Centeno	Centeno	NNP
10_1101-2020_10_26_351783	382	18	E	E	NNP
10_1101-2020_10_26_351783	382	19	,	,	,
10_1101-2020_10_26_351783	382	20	Sanz	Sanz	NNP
10_1101-2020_10_26_351783	382	21	F	F	NNP
10_1101-2020_10_26_351783	382	22	,	,	,
10_1101-2020_10_26_351783	382	23	et	et	NNP
10_1101-2020_10_26_351783	382	24	al	al	NNP
10_1101-2020_10_26_351783	382	25	.	.	.
10_1101-2020_10_26_351783	383	1	The	the	DT
10_1101-2020_10_26_351783	383	2	DisGeNET	DisGeNET	NNP
10_1101-2020_10_26_351783	383	3	knowledge	knowledge	NN
10_1101-2020_10_26_351783	383	4	platform	platform	NN
10_1101-2020_10_26_351783	383	5	for	for	IN
10_1101-2020_10_26_351783	383	6	disease	disease	NNP
10_1101-2020_10_26_351783	383	7	genomics	genomic	NNS
10_1101-2020_10_26_351783	383	8	:	:	:
10_1101-2020_10_26_351783	383	9	2019	2019	CD
10_1101-2020_10_26_351783	383	10	update	update	NN
10_1101-2020_10_26_351783	383	11	.	.	.
10_1101-2020_10_26_351783	384	1	Nucleic	Nucleic	NNP
10_1101-2020_10_26_351783	384	2	Acids	Acids	NNPS
10_1101-2020_10_26_351783	384	3	Res	Res	NNP
10_1101-2020_10_26_351783	384	4	.	.	.
10_1101-2020_10_26_351783	385	1	2020;48	2020;48	UH
10_1101-2020_10_26_351783	385	2	:	:	:
10_1101-2020_10_26_351783	385	3	D845–55	D845–55	NNP
10_1101-2020_10_26_351783	385	4	.	.	.
10_1101-2020_10_26_351783	386	1	21	21	CD
10_1101-2020_10_26_351783	386	2	.	.	.
10_1101-2020_10_26_351783	387	1	Yu	Yu	NNP
10_1101-2020_10_26_351783	387	2	G	G	NNP
10_1101-2020_10_26_351783	387	3	,	,	,
10_1101-2020_10_26_351783	387	4	Wang	Wang	NNP
10_1101-2020_10_26_351783	387	5	LG	LG	NNP
10_1101-2020_10_26_351783	387	6	,	,	,
10_1101-2020_10_26_351783	387	7	Han	Han	NNP
10_1101-2020_10_26_351783	387	8	Y	Y	NNP
10_1101-2020_10_26_351783	387	9	,	,	,
10_1101-2020_10_26_351783	387	10	He	-PRON-	PRP
10_1101-2020_10_26_351783	387	11	QY	QY	NNP
10_1101-2020_10_26_351783	387	12	.	.	.
10_1101-2020_10_26_351783	388	1	ClusterProfiler	ClusterProfiler	NNP
10_1101-2020_10_26_351783	388	2	:	:	:
10_1101-2020_10_26_351783	388	3	An	an	DT
10_1101-2020_10_26_351783	388	4	R	r	NN
10_1101-2020_10_26_351783	388	5	package	package	NN
10_1101-2020_10_26_351783	388	6	for	for	IN
10_1101-2020_10_26_351783	388	7	comparing	compare	VBG
10_1101-2020_10_26_351783	388	8	biological	biological	JJ
10_1101-2020_10_26_351783	388	9	themes	theme	NNS
10_1101-2020_10_26_351783	388	10	among	among	IN
10_1101-2020_10_26_351783	388	11	gene	gene	NN
10_1101-2020_10_26_351783	388	12	clusters	cluster	NNS
10_1101-2020_10_26_351783	388	13	.	.	.
10_1101-2020_10_26_351783	389	1	Omi	Omi	NNP
10_1101-2020_10_26_351783	389	2	A	a	DT
10_1101-2020_10_26_351783	389	3	J	J	NNP
10_1101-2020_10_26_351783	389	4	Integr	Integr	NNP
10_1101-2020_10_26_351783	389	5	Biol	Biol	NNP
10_1101-2020_10_26_351783	389	6	.	.	.
10_1101-2020_10_26_351783	390	1	2012;16:284–7	2012;16:284–7	CD
10_1101-2020_10_26_351783	390	2	.	.	.
10_1101-2020_10_26_351783	391	1	22	22	CD
10_1101-2020_10_26_351783	391	2	.	.	.
10_1101-2020_10_26_351783	392	1	Paul	Paul	NNP
10_1101-2020_10_26_351783	392	2	Shannon	Shannon	NNP
10_1101-2020_10_26_351783	392	3	,	,	,
10_1101-2020_10_26_351783	392	4	Andrew	Andrew	NNP
10_1101-2020_10_26_351783	392	5	Markiel	Markiel	NNP
10_1101-2020_10_26_351783	392	6	,	,	,
10_1101-2020_10_26_351783	392	7	Owen	Owen	NNP
10_1101-2020_10_26_351783	392	8	Ozier	ozier	RBR
10_1101-2020_10_26_351783	392	9	,	,	,
10_1101-2020_10_26_351783	392	10	Nitin	Nitin	NNP
10_1101-2020_10_26_351783	392	11	S.	S.	NNP
10_1101-2020_10_26_351783	392	12	Baliga	Baliga	NNP
10_1101-2020_10_26_351783	392	13	,	,	,
10_1101-2020_10_26_351783	392	14	Jonathan	Jonathan	NNP
10_1101-2020_10_26_351783	392	15	T.	T.	NNP
10_1101-2020_10_26_351783	392	16	Wang	Wang	NNP
10_1101-2020_10_26_351783	392	17	,	,	,
10_1101-2020_10_26_351783	392	18	Daniel	Daniel	NNP
10_1101-2020_10_26_351783	392	19	Ramage	Ramage	NNP
10_1101-2020_10_26_351783	392	20	,	,	,
10_1101-2020_10_26_351783	392	21	Nada	Nada	NNP
10_1101-2020_10_26_351783	392	22	Amin	Amin	NNP
10_1101-2020_10_26_351783	392	23	,	,	,
10_1101-2020_10_26_351783	392	24	Benno	Benno	NNP
10_1101-2020_10_26_351783	392	25	Schwikowski	Schwikowski	NNP
10_1101-2020_10_26_351783	392	26	,	,	,
10_1101-2020_10_26_351783	392	27	and	and	CC
10_1101-2020_10_26_351783	392	28	Trey	Trey	NNP
10_1101-2020_10_26_351783	392	29	Ideker	Ideker	NNP
10_1101-2020_10_26_351783	392	30	.	.	.
10_1101-2020_10_26_351783	393	1	Cytoscape	cytoscape	NN
10_1101-2020_10_26_351783	393	2	:	:	:
10_1101-2020_10_26_351783	393	3	A	a	DT
10_1101-2020_10_26_351783	393	4	Software	Software	NNP
10_1101-2020_10_26_351783	393	5	Environment	Environment	NNP
10_1101-2020_10_26_351783	393	6	for	for	IN
10_1101-2020_10_26_351783	393	7	Integrated	Integrated	NNP
10_1101-2020_10_26_351783	393	8	Models	Models	NNPS
10_1101-2020_10_26_351783	393	9	.	.	.
10_1101-2020_10_26_351783	394	1	Genome	genome	JJ
10_1101-2020_10_26_351783	394	2	Res	res	NN
10_1101-2020_10_26_351783	394	3	[	[	-LRB-
10_1101-2020_10_26_351783	394	4	Internet	internet	NN
10_1101-2020_10_26_351783	394	5	]	]	-RRB-
10_1101-2020_10_26_351783	394	6	.	.	.
10_1101-2020_10_26_351783	395	1	1971;13:426	1971;13:426	CD
10_1101-2020_10_26_351783	395	2	.	.	.
10_1101-2020_10_26_351783	396	1	Available	available	JJ
10_1101-2020_10_26_351783	396	2	from	from	IN
10_1101-2020_10_26_351783	396	3	:	:	:
10_1101-2020_10_26_351783	396	4	http://ci.nii.ac.jp/naid/110001910481/	http://ci.nii.ac.jp/naid/110001910481/	NN
10_1101-2020_10_26_351783	396	5	23	23	CD
10_1101-2020_10_26_351783	396	6	.	.	.
10_1101-2020_10_26_351783	397	1	Maere	Maere	NNP
10_1101-2020_10_26_351783	397	2	S	S	NNP
10_1101-2020_10_26_351783	397	3	,	,	,
10_1101-2020_10_26_351783	397	4	Heymans	Heymans	NNPS
10_1101-2020_10_26_351783	397	5	K	K	NNP
10_1101-2020_10_26_351783	397	6	,	,	,
10_1101-2020_10_26_351783	397	7	Kuiper	Kuiper	NNP
10_1101-2020_10_26_351783	397	8	M.	M.	NNP
10_1101-2020_10_26_351783	397	9	Systems	Systems	NNP
10_1101-2020_10_26_351783	397	10	biology	biology	NN
10_1101-2020_10_26_351783	397	11	BiNGO	BiNGO	NNP
10_1101-2020_10_26_351783	397	12	[	[	-LRB-
10_1101-2020_10_26_351783	397	13	:	:	:
10_1101-2020_10_26_351783	397	14	a	a	DT
10_1101-2020_10_26_351783	397	15	Cytoscape	Cytoscape	NNP
10_1101-2020_10_26_351783	397	16	plugin	plugin	NN
10_1101-2020_10_26_351783	397	17	to	to	TO
10_1101-2020_10_26_351783	397	18	assess	assess	VB
10_1101-2020_10_26_351783	397	19	overrepresentation	overrepresentation	NN
10_1101-2020_10_26_351783	397	20	of	of	IN
10_1101-2020_10_26_351783	397	21	Gene	Gene	NNP
10_1101-2020_10_26_351783	397	22	Ontology	Ontology	NNP
10_1101-2020_10_26_351783	397	23	categories	category	NNS
10_1101-2020_10_26_351783	397	24	in	in	IN
10_1101-2020_10_26_351783	397	25	Biological	Biological	NNP
10_1101-2020_10_26_351783	397	26	Networks	Networks	NNP
10_1101-2020_10_26_351783	397	27	.	.	.
10_1101-2020_10_26_351783	398	1	2005;21:3448–9	2005;21:3448–9	CD
10_1101-2020_10_26_351783	398	2	.	.	.
10_1101-2020_10_26_351783	399	1	24	24	CD
10_1101-2020_10_26_351783	399	2	.	.	.
10_1101-2020_10_26_351783	400	1	Supek	Supek	NNP
10_1101-2020_10_26_351783	400	2	F	F	NNP
10_1101-2020_10_26_351783	400	3	,	,	,
10_1101-2020_10_26_351783	400	4	Bošnjak	Bošnjak	NNP
10_1101-2020_10_26_351783	400	5	M	M	NNP
10_1101-2020_10_26_351783	400	6	,	,	,
10_1101-2020_10_26_351783	400	7	Škunca	Škunca	NNP
10_1101-2020_10_26_351783	400	8	N	N	NNP
10_1101-2020_10_26_351783	400	9	,	,	,
10_1101-2020_10_26_351783	400	10	Šmuc	Šmuc	NNP
10_1101-2020_10_26_351783	400	11	T.	T.	NNP
10_1101-2020_10_26_351783	400	12	Revigo	Revigo	NNP
10_1101-2020_10_26_351783	400	13	summarizes	summarize	VBZ
10_1101-2020_10_26_351783	400	14	and	and	CC
10_1101-2020_10_26_351783	400	15	visualizes	visualize	VBZ
10_1101-2020_10_26_351783	400	16	long	long	JJ
10_1101-2020_10_26_351783	400	17	lists	list	NNS
10_1101-2020_10_26_351783	400	18	of	of	IN
10_1101-2020_10_26_351783	400	19	gene	gene	NN
10_1101-2020_10_26_351783	400	20	ontology	ontology	NN
10_1101-2020_10_26_351783	400	21	terms	term	NNS
10_1101-2020_10_26_351783	400	22	.	.	.
10_1101-2020_10_26_351783	401	1	PLoS	PLoS	:
10_1101-2020_10_26_351783	401	2	One	one	CD
10_1101-2020_10_26_351783	401	3	.	.	.
10_1101-2020_10_26_351783	402	1	2011;6	2011;6	LS
10_1101-2020_10_26_351783	402	2	.	.	.
10_1101-2020_10_26_351783	403	1	25	25	CD
10_1101-2020_10_26_351783	403	2	.	.	.
10_1101-2020_10_26_351783	404	1	Carbone	Carbone	NNP
10_1101-2020_10_26_351783	404	2	F	F	NNP
10_1101-2020_10_26_351783	404	3	,	,	,
10_1101-2020_10_26_351783	404	4	De	De	NNP
10_1101-2020_10_26_351783	404	5	Rosa	Rosa	NNP
10_1101-2020_10_26_351783	404	6	V	V	NNP
10_1101-2020_10_26_351783	404	7	,	,	,
10_1101-2020_10_26_351783	404	8	Carrieri	Carrieri	NNP
10_1101-2020_10_26_351783	404	9	PB	PB	NNP
10_1101-2020_10_26_351783	404	10	,	,	,
10_1101-2020_10_26_351783	404	11	Montella	Montella	NNP
10_1101-2020_10_26_351783	404	12	S	S	NNP
10_1101-2020_10_26_351783	404	13	,	,	,
10_1101-2020_10_26_351783	404	14	Bruzzese	Bruzzese	NNP
10_1101-2020_10_26_351783	404	15	D	d	NN
10_1101-2020_10_26_351783	404	16	,	,	,
10_1101-2020_10_26_351783	404	17	Porcellini	Porcellini	NNP
10_1101-2020_10_26_351783	404	18	A	A	NNP
10_1101-2020_10_26_351783	404	19	,	,	,
10_1101-2020_10_26_351783	404	20	et	et	NNP
10_1101-2020_10_26_351783	404	21	al	al	NNP
10_1101-2020_10_26_351783	404	22	.	.	.
10_1101-2020_10_26_351783	405	1	Regulatory	regulatory	JJ
10_1101-2020_10_26_351783	405	2	T	T	NNP
10_1101-2020_10_26_351783	405	3	cell	cell	NN
10_1101-2020_10_26_351783	405	4	proliferative	proliferative	JJ
10_1101-2020_10_26_351783	405	5	potential	potential	NN
10_1101-2020_10_26_351783	405	6	is	be	VBZ
10_1101-2020_10_26_351783	405	7	impaired	impair	VBN
10_1101-2020_10_26_351783	405	8	in	in	IN
10_1101-2020_10_26_351783	405	9	human	human	JJ
10_1101-2020_10_26_351783	405	10	autoimmune	autoimmune	JJ
10_1101-2020_10_26_351783	405	11	disease	disease	NN
10_1101-2020_10_26_351783	405	12	.	.	.
10_1101-2020_10_26_351783	406	1	Nat	Nat	NNP
10_1101-2020_10_26_351783	406	2	Med	Med	NNP
10_1101-2020_10_26_351783	406	3	.	.	.
10_1101-2020_10_26_351783	407	1	2014;20:69–74	2014;20:69–74	CD
10_1101-2020_10_26_351783	407	2	.	.	.
10_1101-2020_10_26_351783	408	1	26	26	CD
10_1101-2020_10_26_351783	408	2	.	.	.
10_1101-2020_10_26_351783	409	1	Mammana	Mammana	NNP
10_1101-2020_10_26_351783	409	2	S	S	NNP
10_1101-2020_10_26_351783	409	3	,	,	,
10_1101-2020_10_26_351783	409	4	Bramanti	Bramanti	NNP
10_1101-2020_10_26_351783	409	5	P	P	NNP
10_1101-2020_10_26_351783	409	6	,	,	,
10_1101-2020_10_26_351783	409	7	Mazzon	Mazzon	NNP
10_1101-2020_10_26_351783	409	8	E	E	NNP
10_1101-2020_10_26_351783	409	9	,	,	,
10_1101-2020_10_26_351783	409	10	Cavalli	Cavalli	NNP
10_1101-2020_10_26_351783	409	11	E	E	NNP
10_1101-2020_10_26_351783	409	12	,	,	,
10_1101-2020_10_26_351783	409	13	Basile	Basile	NNP
10_1101-2020_10_26_351783	409	14	MS	MS	NNP
10_1101-2020_10_26_351783	409	15	,	,	,
10_1101-2020_10_26_351783	409	16	Fagone	Fagone	NNP
10_1101-2020_10_26_351783	409	17	P	P	NNP
10_1101-2020_10_26_351783	409	18	,	,	,
10_1101-2020_10_26_351783	409	19	et	et	NNP
10_1101-2020_10_26_351783	409	20	al	al	NNP
10_1101-2020_10_26_351783	409	21	.	.	.
10_1101-2020_10_26_351783	410	1	Preclinical	preclinical	JJ
10_1101-2020_10_26_351783	410	2	evaluation	evaluation	NN
10_1101-2020_10_26_351783	410	3	of	of	IN
10_1101-2020_10_26_351783	410	4	the	the	DT
10_1101-2020_10_26_351783	410	5	PI3K	PI3K	NNP
10_1101-2020_10_26_351783	410	6	/	/	SYM
10_1101-2020_10_26_351783	410	7	Akt	Akt	NNP
10_1101-2020_10_26_351783	410	8	/	/	SYM
10_1101-2020_10_26_351783	410	9	mTOR	mtor	NN
10_1101-2020_10_26_351783	410	10	pathway	pathway	NN
10_1101-2020_10_26_351783	410	11	in	in	IN
10_1101-2020_10_26_351783	410	12	animal	animal	NN
10_1101-2020_10_26_351783	410	13	models	model	NNS
10_1101-2020_10_26_351783	410	14	of	of	IN
10_1101-2020_10_26_351783	410	15	multiple	multiple	JJ
10_1101-2020_10_26_351783	410	16	sclerosis	sclerosis	NN
10_1101-2020_10_26_351783	410	17	.	.	.
10_1101-2020_10_26_351783	411	1	Oncotarget	oncotarget	RB
10_1101-2020_10_26_351783	411	2	.	.	.
10_1101-2020_10_26_351783	412	1	2018;9:8263–77	2018;9:8263–77	LS
10_1101-2020_10_26_351783	412	2	.	.	.
10_1101-2020_10_26_351783	413	1	27	27	CD
10_1101-2020_10_26_351783	413	2	.	.	.
10_1101-2020_10_26_351783	414	1	Holley	Holley	NNP
10_1101-2020_10_26_351783	414	2	JE	JE	NNP
10_1101-2020_10_26_351783	414	3	,	,	,
10_1101-2020_10_26_351783	414	4	Gveric	Gveric	NNP
10_1101-2020_10_26_351783	414	5	D	D	NNP
10_1101-2020_10_26_351783	414	6	,	,	,
10_1101-2020_10_26_351783	414	7	Newcombe	Newcombe	NNP
10_1101-2020_10_26_351783	414	8	J	J	NNP
10_1101-2020_10_26_351783	414	9	,	,	,
10_1101-2020_10_26_351783	414	10	Cuzner	Cuzner	NNP
10_1101-2020_10_26_351783	414	11	ML	ML	NNP
10_1101-2020_10_26_351783	414	12	,	,	,
10_1101-2020_10_26_351783	414	13	Gutowski	Gutowski	NNP
10_1101-2020_10_26_351783	414	14	NJ	NJ	NNP
10_1101-2020_10_26_351783	414	15	.	.	.
10_1101-2020_10_26_351783	415	1	Astrocyte	astrocyte	IN
10_1101-2020_10_26_351783	415	2	characterization	characterization	NN
10_1101-2020_10_26_351783	415	3	in	in	IN
10_1101-2020_10_26_351783	415	4	the	the	DT
10_1101-2020_10_26_351783	415	5	(	(	-LRB-
10_1101-2020_10_26_351783	415	6	which	which	WDT
10_1101-2020_10_26_351783	415	7	was	be	VBD
10_1101-2020_10_26_351783	415	8	not	not	RB
10_1101-2020_10_26_351783	415	9	certified	certify	VBN
10_1101-2020_10_26_351783	415	10	by	by	IN
10_1101-2020_10_26_351783	415	11	peer	peer	NN
10_1101-2020_10_26_351783	415	12	review	review	NN
10_1101-2020_10_26_351783	415	13	)	)	-RRB-
10_1101-2020_10_26_351783	415	14	is	be	VBZ
10_1101-2020_10_26_351783	415	15	the	the	DT
10_1101-2020_10_26_351783	415	16	author	author	NN
10_1101-2020_10_26_351783	415	17	/	/	SYM
10_1101-2020_10_26_351783	415	18	funder	funder	NN
10_1101-2020_10_26_351783	415	19	.	.	.
10_1101-2020_10_26_351783	416	1	All	all	DT
10_1101-2020_10_26_351783	416	2	rights	right	NNS
10_1101-2020_10_26_351783	416	3	reserved	reserve	VBD
10_1101-2020_10_26_351783	416	4	.	.	.
10_1101-2020_10_26_351783	417	1	No	no	DT
10_1101-2020_10_26_351783	417	2	reuse	reuse	NN
10_1101-2020_10_26_351783	417	3	allowed	allow	VBN
10_1101-2020_10_26_351783	417	4	without	without	IN
10_1101-2020_10_26_351783	417	5	permission	permission	NN
10_1101-2020_10_26_351783	417	6	.	.	.
10_1101-2020_10_26_351783	418	1	The	the	DT
10_1101-2020_10_26_351783	418	2	copyright	copyright	NN
10_1101-2020_10_26_351783	418	3	holder	holder	NN
10_1101-2020_10_26_351783	418	4	for	for	IN
10_1101-2020_10_26_351783	418	5	this	this	DT
10_1101-2020_10_26_351783	418	6	preprintthis	preprintthis	NN
10_1101-2020_10_26_351783	418	7	version	version	NN
10_1101-2020_10_26_351783	418	8	posted	post	VBD
10_1101-2020_10_26_351783	418	9	January	January	NNP
10_1101-2020_10_26_351783	418	10	6	6	CD
10_1101-2020_10_26_351783	418	11	,	,	,
10_1101-2020_10_26_351783	418	12	2021	2021	CD
10_1101-2020_10_26_351783	418	13	.	.	.
10_1101-2020_10_26_351783	418	14	;	;	:
10_1101-2020_10_26_351783	418	15	https://doi.org/10.1101/2020.10.26.351783doi	https://doi.org/10.1101/2020.10.26.351783doi	ADD
10_1101-2020_10_26_351783	418	16	:	:	:
10_1101-2020_10_26_351783	418	17	bioRxiv	biorxiv	IN
10_1101-2020_10_26_351783	418	18	preprint	preprint	NN
10_1101-2020_10_26_351783	418	19	https://doi.org/10.1101/2020.10.26.351783	https://doi.org/10.1101/2020.10.26.351783	CC
10_1101-2020_10_26_351783	418	20	23	23	CD
10_1101-2020_10_26_351783	418	21	multiple	multiple	JJ
10_1101-2020_10_26_351783	418	22	sclerosis	sclerosis	NN
10_1101-2020_10_26_351783	418	23	glial	glial	NN
10_1101-2020_10_26_351783	418	24	scar	scar	NN
10_1101-2020_10_26_351783	418	25	.	.	.
10_1101-2020_10_26_351783	419	1	Neuropathol	Neuropathol	NNP
10_1101-2020_10_26_351783	419	2	Appl	Appl	NNP
10_1101-2020_10_26_351783	419	3	Neurobiol	Neurobiol	NNP
10_1101-2020_10_26_351783	419	4	.	.	.
10_1101-2020_10_26_351783	420	1	2003;29:434–44	2003;29:434–44	LS
10_1101-2020_10_26_351783	420	2	.	.	.
10_1101-2020_10_26_351783	421	1	28	28	CD
10_1101-2020_10_26_351783	421	2	.	.	.
10_1101-2020_10_26_351783	422	1	Pedotti	Pedotti	NNP
10_1101-2020_10_26_351783	422	2	R	R	NNP
10_1101-2020_10_26_351783	422	3	,	,	,
10_1101-2020_10_26_351783	422	4	DeVoss	DeVoss	NNP
10_1101-2020_10_26_351783	422	5	JJ	JJ	NNP
10_1101-2020_10_26_351783	422	6	,	,	,
10_1101-2020_10_26_351783	422	7	Youssef	Youssef	NNP
10_1101-2020_10_26_351783	422	8	S	S	NNP
10_1101-2020_10_26_351783	422	9	,	,	,
10_1101-2020_10_26_351783	422	10	Mitchell	Mitchell	NNP
10_1101-2020_10_26_351783	422	11	D	D	NNP
10_1101-2020_10_26_351783	422	12	,	,	,
10_1101-2020_10_26_351783	422	13	Wedemeyer	Wedemeyer	NNP
10_1101-2020_10_26_351783	422	14	J	J	NNP
10_1101-2020_10_26_351783	422	15	,	,	,
10_1101-2020_10_26_351783	422	16	Madanat	Madanat	NNP
10_1101-2020_10_26_351783	422	17	R	R	NNP
10_1101-2020_10_26_351783	422	18	,	,	,
10_1101-2020_10_26_351783	422	19	et	et	NNP
10_1101-2020_10_26_351783	422	20	al	al	NNP
10_1101-2020_10_26_351783	422	21	.	.	.
10_1101-2020_10_26_351783	423	1	Multiple	multiple	JJ
10_1101-2020_10_26_351783	423	2	elements	element	NNS
10_1101-2020_10_26_351783	423	3	of	of	IN
10_1101-2020_10_26_351783	423	4	the	the	DT
10_1101-2020_10_26_351783	423	5	allergic	allergic	JJ
10_1101-2020_10_26_351783	423	6	arm	arm	NN
10_1101-2020_10_26_351783	423	7	of	of	IN
10_1101-2020_10_26_351783	423	8	the	the	DT
10_1101-2020_10_26_351783	423	9	immune	immune	JJ
10_1101-2020_10_26_351783	423	10	response	response	NN
10_1101-2020_10_26_351783	423	11	modulate	modulate	NNP
10_1101-2020_10_26_351783	423	12	autoimmune	autoimmune	JJ
10_1101-2020_10_26_351783	423	13	demyelination	demyelination	NN
10_1101-2020_10_26_351783	423	14	.	.	.
10_1101-2020_10_26_351783	424	1	Proc	Proc	NNP
10_1101-2020_10_26_351783	424	2	Natl	Natl	NNP
10_1101-2020_10_26_351783	424	3	Acad	Acad	NNP
10_1101-2020_10_26_351783	424	4	Sci	Sci	NNP
10_1101-2020_10_26_351783	424	5	U	U	NNP
10_1101-2020_10_26_351783	424	6	S	S	NNP
10_1101-2020_10_26_351783	424	7	A.	a.	NN
10_1101-2020_10_26_351783	425	1	2003;100:1867–72	2003;100:1867–72	LS
10_1101-2020_10_26_351783	425	2	.	.	.
10_1101-2020_10_26_351783	426	1	29	29	CD
10_1101-2020_10_26_351783	426	2	.	.	.
10_1101-2020_10_26_351783	427	1	Cui	Cui	NNP
10_1101-2020_10_26_351783	427	2	LY	LY	NNP
10_1101-2020_10_26_351783	427	3	,	,	,
10_1101-2020_10_26_351783	427	4	Chu	Chu	NNP
10_1101-2020_10_26_351783	427	5	SF	SF	NNP
10_1101-2020_10_26_351783	427	6	,	,	,
10_1101-2020_10_26_351783	427	7	Chen	Chen	NNP
10_1101-2020_10_26_351783	427	8	NH	NH	NNP
10_1101-2020_10_26_351783	427	9	.	.	.
10_1101-2020_10_26_351783	428	1	The	the	DT
10_1101-2020_10_26_351783	428	2	role	role	NN
10_1101-2020_10_26_351783	428	3	of	of	IN
10_1101-2020_10_26_351783	428	4	chemokines	chemokine	NNS
10_1101-2020_10_26_351783	428	5	and	and	CC
10_1101-2020_10_26_351783	428	6	chemokine	chemokine	NNP
10_1101-2020_10_26_351783	428	7	receptors	receptor	NNS
10_1101-2020_10_26_351783	428	8	in	in	IN
10_1101-2020_10_26_351783	428	9	multiple	multiple	JJ
10_1101-2020_10_26_351783	428	10	sclerosis	sclerosis	NN
10_1101-2020_10_26_351783	428	11	.	.	.
10_1101-2020_10_26_351783	429	1	Int	Int	NNP
10_1101-2020_10_26_351783	429	2	Immunopharmacol	Immunopharmacol	NNP
10_1101-2020_10_26_351783	429	3	[	[	-LRB-
10_1101-2020_10_26_351783	429	4	Internet	internet	NN
10_1101-2020_10_26_351783	429	5	]	]	-RRB-
10_1101-2020_10_26_351783	429	6	.	.	.
10_1101-2020_10_26_351783	430	1	Elsevier	elsevier	NN
10_1101-2020_10_26_351783	430	2	;	;	:
10_1101-2020_10_26_351783	430	3	2020;83:106314	2020;83:106314	CD
10_1101-2020_10_26_351783	430	4	.	.	.
10_1101-2020_10_26_351783	431	1	Available	available	JJ
10_1101-2020_10_26_351783	431	2	from	from	IN
10_1101-2020_10_26_351783	431	3	:	:	:
10_1101-2020_10_26_351783	431	4	https://doi.org/10.1016/j.intimp.2020.106314	https://doi.org/10.1016/j.intimp.2020.106314	NNP
10_1101-2020_10_26_351783	431	5	30	30	CD
10_1101-2020_10_26_351783	431	6	.	.	.
10_1101-2020_10_26_351783	432	1	Krumbholz	Krumbholz	NNP
10_1101-2020_10_26_351783	432	2	M	M	NNP
10_1101-2020_10_26_351783	432	3	,	,	,
10_1101-2020_10_26_351783	432	4	Theil	Theil	NNP
10_1101-2020_10_26_351783	432	5	D	D	NNP
10_1101-2020_10_26_351783	432	6	,	,	,
10_1101-2020_10_26_351783	432	7	Cepok	Cepok	NNP
10_1101-2020_10_26_351783	432	8	S	S	NNP
10_1101-2020_10_26_351783	432	9	,	,	,
10_1101-2020_10_26_351783	432	10	Hemmer	Hemmer	NNP
10_1101-2020_10_26_351783	432	11	B	B	NNP
10_1101-2020_10_26_351783	432	12	,	,	,
10_1101-2020_10_26_351783	432	13	Kivisäkk	Kivisäkk	NNP
10_1101-2020_10_26_351783	432	14	P	P	NNP
10_1101-2020_10_26_351783	432	15	,	,	,
10_1101-2020_10_26_351783	432	16	Ransohoff	Ransohoff	NNP
10_1101-2020_10_26_351783	432	17	RM	RM	NNP
10_1101-2020_10_26_351783	432	18	,	,	,
10_1101-2020_10_26_351783	432	19	et	et	NNP
10_1101-2020_10_26_351783	432	20	al	al	NNP
10_1101-2020_10_26_351783	432	21	.	.	.
10_1101-2020_10_26_351783	433	1	Chemokines	chemokine	NNS
10_1101-2020_10_26_351783	433	2	in	in	IN
10_1101-2020_10_26_351783	433	3	multiple	multiple	JJ
10_1101-2020_10_26_351783	433	4	sclerosis	sclerosis	NN
10_1101-2020_10_26_351783	433	5	:	:	:
10_1101-2020_10_26_351783	433	6	CXCL12	cxcl12	NN
10_1101-2020_10_26_351783	433	7	and	and	CC
10_1101-2020_10_26_351783	433	8	CXCL13	CXCL13	NNP
10_1101-2020_10_26_351783	433	9	up	up	IN
10_1101-2020_10_26_351783	433	10	-	-	HYPH
10_1101-2020_10_26_351783	433	11	regulation	regulation	NN
10_1101-2020_10_26_351783	433	12	is	be	VBZ
10_1101-2020_10_26_351783	433	13	differentially	differentially	RB
10_1101-2020_10_26_351783	433	14	linked	link	VBN
10_1101-2020_10_26_351783	433	15	to	to	IN
10_1101-2020_10_26_351783	433	16	CNS	CNS	NNP
10_1101-2020_10_26_351783	433	17	immune	immune	JJ
10_1101-2020_10_26_351783	433	18	cell	cell	NN
10_1101-2020_10_26_351783	433	19	recruitment	recruitment	NN
10_1101-2020_10_26_351783	433	20	.	.	.
10_1101-2020_10_26_351783	434	1	Brain	brain	NN
10_1101-2020_10_26_351783	434	2	.	.	.
10_1101-2020_10_26_351783	435	1	2006;129:200–11	2006;129:200–11	CD
10_1101-2020_10_26_351783	435	2	.	.	.
10_1101-2020_10_26_351783	436	1	31	31	CD
10_1101-2020_10_26_351783	436	2	.	.	.
10_1101-2020_10_26_351783	437	1	Krementsov	Krementsov	NNP
10_1101-2020_10_26_351783	437	2	DN	DN	NNP
10_1101-2020_10_26_351783	437	3	,	,	,
10_1101-2020_10_26_351783	437	4	Thornton	Thornton	NNP
10_1101-2020_10_26_351783	437	5	TM	TM	NNP
10_1101-2020_10_26_351783	437	6	,	,	,
10_1101-2020_10_26_351783	437	7	Teuscher	Teuscher	NNP
10_1101-2020_10_26_351783	437	8	C	C	NNP
10_1101-2020_10_26_351783	437	9	,	,	,
10_1101-2020_10_26_351783	437	10	Rincon	Rincon	NNP
10_1101-2020_10_26_351783	437	11	M.	M.	NNP
10_1101-2020_10_26_351783	437	12	The	The	NNP
10_1101-2020_10_26_351783	437	13	Emerging	Emerging	NNP
10_1101-2020_10_26_351783	437	14	Role	Role	NNP
10_1101-2020_10_26_351783	437	15	of	of	IN
10_1101-2020_10_26_351783	437	16	p38	p38	NN
10_1101-2020_10_26_351783	437	17	Mitogen-	Mitogen-	NNP
10_1101-2020_10_26_351783	437	18	Activated	Activated	NNP
10_1101-2020_10_26_351783	437	19	Protein	Protein	NNP
10_1101-2020_10_26_351783	437	20	Kinase	Kinase	NNP
10_1101-2020_10_26_351783	437	21	in	in	IN
10_1101-2020_10_26_351783	437	22	Multiple	Multiple	NNP
10_1101-2020_10_26_351783	437	23	Sclerosis	Sclerosis	NNP
10_1101-2020_10_26_351783	437	24	and	and	CC
10_1101-2020_10_26_351783	437	25	Its	-PRON-	PRP$
10_1101-2020_10_26_351783	437	26	Models	model	NNS
10_1101-2020_10_26_351783	437	27	.	.	.
10_1101-2020_10_26_351783	438	1	Mol	Mol	NNP
10_1101-2020_10_26_351783	438	2	Cell	Cell	NNP
10_1101-2020_10_26_351783	438	3	Biol	Biol	NNP
10_1101-2020_10_26_351783	438	4	.	.	.
10_1101-2020_10_26_351783	439	1	2013;33:3728–34	2013;33:3728–34	CD
10_1101-2020_10_26_351783	439	2	.	.	.
10_1101-2020_10_26_351783	440	1	32	32	CD
10_1101-2020_10_26_351783	440	2	.	.	.
10_1101-2020_10_26_351783	441	1	Kotelnikova	Kotelnikova	NNP
10_1101-2020_10_26_351783	441	2	E	E	NNP
10_1101-2020_10_26_351783	441	3	,	,	,
10_1101-2020_10_26_351783	441	4	Kiani	Kiani	NNP
10_1101-2020_10_26_351783	441	5	NA	NA	NNP
10_1101-2020_10_26_351783	441	6	,	,	,
10_1101-2020_10_26_351783	441	7	Messinis	Messinis	NNP
10_1101-2020_10_26_351783	441	8	D	D	NNP
10_1101-2020_10_26_351783	441	9	,	,	,
10_1101-2020_10_26_351783	441	10	Pertsovskaya	Pertsovskaya	NNP
10_1101-2020_10_26_351783	441	11	I	I	NNP
10_1101-2020_10_26_351783	441	12	,	,	,
10_1101-2020_10_26_351783	441	13	Pliaka	Pliaka	NNP
10_1101-2020_10_26_351783	441	14	V	V	NNP
10_1101-2020_10_26_351783	441	15	,	,	,
10_1101-2020_10_26_351783	441	16	Bernardo	Bernardo	NNP
10_1101-2020_10_26_351783	441	17	-	-	HYPH
10_1101-2020_10_26_351783	441	18	Faura	Faura	NNP
10_1101-2020_10_26_351783	441	19	M	M	NNP
10_1101-2020_10_26_351783	441	20	,	,	,
10_1101-2020_10_26_351783	441	21	et	et	NNP
10_1101-2020_10_26_351783	441	22	al	al	NNP
10_1101-2020_10_26_351783	441	23	.	.	.
10_1101-2020_10_26_351783	442	1	MAPK	MAPK	NNP
10_1101-2020_10_26_351783	442	2	pathway	pathway	NN
10_1101-2020_10_26_351783	442	3	and	and	CC
10_1101-2020_10_26_351783	442	4	B	b	NN
10_1101-2020_10_26_351783	442	5	cells	cell	NNS
10_1101-2020_10_26_351783	442	6	overactivation	overactivation	NN
10_1101-2020_10_26_351783	442	7	in	in	IN
10_1101-2020_10_26_351783	442	8	multiple	multiple	JJ
10_1101-2020_10_26_351783	442	9	sclerosis	sclerosis	NN
10_1101-2020_10_26_351783	442	10	revealed	reveal	VBN
10_1101-2020_10_26_351783	442	11	by	by	IN
10_1101-2020_10_26_351783	442	12	phosphoproteomics	phosphoproteomic	NNS
10_1101-2020_10_26_351783	442	13	and	and	CC
10_1101-2020_10_26_351783	442	14	genomic	genomic	JJ
10_1101-2020_10_26_351783	442	15	analysis	analysis	NN
10_1101-2020_10_26_351783	442	16	.	.	.
10_1101-2020_10_26_351783	443	1	Proc	Proc	NNP
10_1101-2020_10_26_351783	443	2	Natl	Natl	NNP
10_1101-2020_10_26_351783	443	3	Acad	Acad	NNP
10_1101-2020_10_26_351783	443	4	Sci	Sci	NNP
10_1101-2020_10_26_351783	443	5	U	U	NNP
10_1101-2020_10_26_351783	443	6	S	S	NNP
10_1101-2020_10_26_351783	443	7	A.	a.	NN
10_1101-2020_10_26_351783	444	1	2019;116:9671–6	2019;116:9671–6	CD
10_1101-2020_10_26_351783	444	2	.	.	.
10_1101-2020_10_26_351783	445	1	33	33	CD
10_1101-2020_10_26_351783	445	2	.	.	.
10_1101-2020_10_26_351783	446	1	Kunkl	Kunkl	NNP
10_1101-2020_10_26_351783	446	2	M	M	NNP
10_1101-2020_10_26_351783	446	3	,	,	,
10_1101-2020_10_26_351783	446	4	Frascolla	Frascolla	NNP
10_1101-2020_10_26_351783	446	5	S	S	NNP
10_1101-2020_10_26_351783	446	6	,	,	,
10_1101-2020_10_26_351783	446	7	Amormino	Amormino	NNP
10_1101-2020_10_26_351783	446	8	C	C	NNP
10_1101-2020_10_26_351783	446	9	,	,	,
10_1101-2020_10_26_351783	446	10	Volpe	Volpe	NNP
10_1101-2020_10_26_351783	446	11	E	E	NNP
10_1101-2020_10_26_351783	446	12	,	,	,
10_1101-2020_10_26_351783	446	13	Tuosto	Tuosto	NNP
10_1101-2020_10_26_351783	446	14	L.	L.	NNP
10_1101-2020_10_26_351783	446	15	T	T	NNP
10_1101-2020_10_26_351783	446	16	Helper	helper	NN
10_1101-2020_10_26_351783	446	17	Cells	Cells	NNPS
10_1101-2020_10_26_351783	446	18	:	:	:
10_1101-2020_10_26_351783	446	19	The	the	DT
10_1101-2020_10_26_351783	446	20	Modulators	modulator	NNS
10_1101-2020_10_26_351783	446	21	of	of	IN
10_1101-2020_10_26_351783	446	22	Inflammation	inflammation	NN
10_1101-2020_10_26_351783	446	23	in	in	IN
10_1101-2020_10_26_351783	446	24	Multiple	Multiple	NNP
10_1101-2020_10_26_351783	446	25	Sclerosis	Sclerosis	NNP
10_1101-2020_10_26_351783	446	26	.	.	.
10_1101-2020_10_26_351783	447	1	Cells	cell	NNS
10_1101-2020_10_26_351783	447	2	.	.	.
10_1101-2020_10_26_351783	448	1	2020;9:482	2020;9:482	CD
10_1101-2020_10_26_351783	448	2	.	.	.
10_1101-2020_10_26_351783	449	1	34	34	CD
10_1101-2020_10_26_351783	449	2	.	.	.
10_1101-2020_10_26_351783	450	1	Waubant	waubant	NN
10_1101-2020_10_26_351783	450	2	E	E	NNP
10_1101-2020_10_26_351783	450	3	,	,	,
10_1101-2020_10_26_351783	450	4	Lucas	Lucas	NNP
10_1101-2020_10_26_351783	450	5	R	R	NNP
10_1101-2020_10_26_351783	450	6	,	,	,
10_1101-2020_10_26_351783	450	7	Mowry	Mowry	NNP
10_1101-2020_10_26_351783	450	8	E	E	NNP
10_1101-2020_10_26_351783	450	9	,	,	,
10_1101-2020_10_26_351783	450	10	Graves	Graves	NNP
10_1101-2020_10_26_351783	450	11	J	J	NNP
10_1101-2020_10_26_351783	450	12	,	,	,
10_1101-2020_10_26_351783	450	13	Olsson	Olsson	NNP
10_1101-2020_10_26_351783	450	14	T	T	NNP
10_1101-2020_10_26_351783	450	15	,	,	,
10_1101-2020_10_26_351783	450	16	Alfredsson	Alfredsson	NNP
10_1101-2020_10_26_351783	450	17	L	L	NNP
10_1101-2020_10_26_351783	450	18	,	,	,
10_1101-2020_10_26_351783	450	19	et	et	NNP
10_1101-2020_10_26_351783	450	20	al	al	NNP
10_1101-2020_10_26_351783	450	21	.	.	.
10_1101-2020_10_26_351783	451	1	Environmental	environmental	JJ
10_1101-2020_10_26_351783	451	2	and	and	CC
10_1101-2020_10_26_351783	451	3	genetic	genetic	JJ
10_1101-2020_10_26_351783	451	4	risk	risk	NN
10_1101-2020_10_26_351783	451	5	factors	factor	NNS
10_1101-2020_10_26_351783	451	6	for	for	IN
10_1101-2020_10_26_351783	451	7	MS	MS	NNP
10_1101-2020_10_26_351783	451	8	:	:	:
10_1101-2020_10_26_351783	451	9	an	an	DT
10_1101-2020_10_26_351783	451	10	integrated	integrated	JJ
10_1101-2020_10_26_351783	451	11	review	review	NN
10_1101-2020_10_26_351783	451	12	.	.	.
10_1101-2020_10_26_351783	452	1	Ann	Ann	NNP
10_1101-2020_10_26_351783	452	2	Clin	Clin	NNP
10_1101-2020_10_26_351783	452	3	Transl	Transl	NNP
10_1101-2020_10_26_351783	452	4	Neurol	Neurol	NNP
10_1101-2020_10_26_351783	452	5	.	.	.
10_1101-2020_10_26_351783	453	1	2019;6:1905–22	2019;6:1905–22	LS
10_1101-2020_10_26_351783	453	2	.	.	.
10_1101-2020_10_26_351783	454	1	35	35	CD
10_1101-2020_10_26_351783	454	2	.	.	.
10_1101-2020_10_26_351783	455	1	Olsson	Olsson	NNP
10_1101-2020_10_26_351783	455	2	T	T	NNP
10_1101-2020_10_26_351783	455	3	,	,	,
10_1101-2020_10_26_351783	455	4	Barcellos	Barcellos	NNP
10_1101-2020_10_26_351783	455	5	LF	LF	NNP
10_1101-2020_10_26_351783	455	6	,	,	,
10_1101-2020_10_26_351783	455	7	Alfredsson	Alfredsson	NNP
10_1101-2020_10_26_351783	455	8	L.	L.	NNP
10_1101-2020_10_26_351783	455	9	Interactions	Interactions	NNP
10_1101-2020_10_26_351783	455	10	between	between	IN
10_1101-2020_10_26_351783	455	11	genetic	genetic	JJ
10_1101-2020_10_26_351783	455	12	,	,	,
10_1101-2020_10_26_351783	455	13	lifestyle	lifestyle	NN
10_1101-2020_10_26_351783	455	14	and	and	CC
10_1101-2020_10_26_351783	455	15	environmental	environmental	JJ
10_1101-2020_10_26_351783	455	16	risk	risk	NN
10_1101-2020_10_26_351783	455	17	factors	factor	NNS
10_1101-2020_10_26_351783	455	18	for	for	IN
10_1101-2020_10_26_351783	455	19	multiple	multiple	JJ
10_1101-2020_10_26_351783	455	20	sclerosis	sclerosis	NN
10_1101-2020_10_26_351783	455	21	.	.	.
10_1101-2020_10_26_351783	456	1	Nat	Nat	NNP
10_1101-2020_10_26_351783	456	2	Rev	Rev	NNP
10_1101-2020_10_26_351783	456	3	Neurol	Neurol	NNP
10_1101-2020_10_26_351783	456	4	.	.	.
10_1101-2020_10_26_351783	457	1	Nature	Nature	NNP
10_1101-2020_10_26_351783	457	2	Publishing	Publishing	NNP
10_1101-2020_10_26_351783	457	3	Group	Group	NNP
10_1101-2020_10_26_351783	457	4	;	;	:
10_1101-2020_10_26_351783	457	5	2016;13:26–36	2016;13:26–36	CD
10_1101-2020_10_26_351783	457	6	.	.	.
10_1101-2020_10_26_351783	458	1	36	36	CD
10_1101-2020_10_26_351783	458	2	.	.	.
10_1101-2020_10_26_351783	459	1	Kular	Kular	NNP
10_1101-2020_10_26_351783	459	2	L	L	NNP
10_1101-2020_10_26_351783	459	3	,	,	,
10_1101-2020_10_26_351783	459	4	Liu	Liu	NNP
10_1101-2020_10_26_351783	459	5	Y	Y	NNP
10_1101-2020_10_26_351783	459	6	,	,	,
10_1101-2020_10_26_351783	459	7	Ruhrmann	Ruhrmann	NNP
10_1101-2020_10_26_351783	459	8	S	S	NNP
10_1101-2020_10_26_351783	459	9	,	,	,
10_1101-2020_10_26_351783	459	10	Zheleznyakova	Zheleznyakova	NNP
10_1101-2020_10_26_351783	459	11	G	G	NNP
10_1101-2020_10_26_351783	459	12	,	,	,
10_1101-2020_10_26_351783	459	13	Marabita	Marabita	NNP
10_1101-2020_10_26_351783	459	14	F	F	NNP
10_1101-2020_10_26_351783	459	15	,	,	,
10_1101-2020_10_26_351783	459	16	Gomez	Gomez	NNP
10_1101-2020_10_26_351783	459	17	-	-	HYPH
10_1101-2020_10_26_351783	459	18	Cabrero	Cabrero	NNP
10_1101-2020_10_26_351783	459	19	D	D	NNP
10_1101-2020_10_26_351783	459	20	,	,	,
10_1101-2020_10_26_351783	459	21	et	et	NNP
10_1101-2020_10_26_351783	459	22	al	al	NNP
10_1101-2020_10_26_351783	459	23	.	.	.
10_1101-2020_10_26_351783	460	1	DNA	dna	NN
10_1101-2020_10_26_351783	460	2	methylation	methylation	NN
10_1101-2020_10_26_351783	460	3	as	as	IN
10_1101-2020_10_26_351783	460	4	a	a	DT
10_1101-2020_10_26_351783	460	5	mediator	mediator	NN
10_1101-2020_10_26_351783	460	6	of	of	IN
10_1101-2020_10_26_351783	460	7	HLA	HLA	NNP
10_1101-2020_10_26_351783	460	8	-	-	HYPH
10_1101-2020_10_26_351783	460	9	DRB1	DRB1	NNP
10_1101-2020_10_26_351783	460	10	15:01	15:01	CD
10_1101-2020_10_26_351783	460	11	and	and	CC
10_1101-2020_10_26_351783	460	12	a	a	DT
10_1101-2020_10_26_351783	460	13	protective	protective	JJ
10_1101-2020_10_26_351783	460	14	variant	variant	NN
10_1101-2020_10_26_351783	460	15	in	in	IN
10_1101-2020_10_26_351783	460	16	multiple	multiple	JJ
10_1101-2020_10_26_351783	460	17	sclerosis	sclerosis	NN
10_1101-2020_10_26_351783	460	18	.	.	.
10_1101-2020_10_26_351783	461	1	Nat	Nat	NNP
10_1101-2020_10_26_351783	461	2	(	(	-LRB-
10_1101-2020_10_26_351783	461	3	which	which	WDT
10_1101-2020_10_26_351783	461	4	was	be	VBD
10_1101-2020_10_26_351783	461	5	not	not	RB
10_1101-2020_10_26_351783	461	6	certified	certify	VBN
10_1101-2020_10_26_351783	461	7	by	by	IN
10_1101-2020_10_26_351783	461	8	peer	peer	NN
10_1101-2020_10_26_351783	461	9	review	review	NN
10_1101-2020_10_26_351783	461	10	)	)	-RRB-
10_1101-2020_10_26_351783	461	11	is	be	VBZ
10_1101-2020_10_26_351783	461	12	the	the	DT
10_1101-2020_10_26_351783	461	13	author	author	NN
10_1101-2020_10_26_351783	461	14	/	/	SYM
10_1101-2020_10_26_351783	461	15	funder	funder	NN
10_1101-2020_10_26_351783	461	16	.	.	.
10_1101-2020_10_26_351783	462	1	All	all	DT
10_1101-2020_10_26_351783	462	2	rights	right	NNS
10_1101-2020_10_26_351783	462	3	reserved	reserve	VBD
10_1101-2020_10_26_351783	462	4	.	.	.
10_1101-2020_10_26_351783	463	1	No	no	DT
10_1101-2020_10_26_351783	463	2	reuse	reuse	NN
10_1101-2020_10_26_351783	463	3	allowed	allow	VBN
10_1101-2020_10_26_351783	463	4	without	without	IN
10_1101-2020_10_26_351783	463	5	permission	permission	NN
10_1101-2020_10_26_351783	463	6	.	.	.
10_1101-2020_10_26_351783	464	1	The	the	DT
10_1101-2020_10_26_351783	464	2	copyright	copyright	NN
10_1101-2020_10_26_351783	464	3	holder	holder	NN
10_1101-2020_10_26_351783	464	4	for	for	IN
10_1101-2020_10_26_351783	464	5	this	this	DT
10_1101-2020_10_26_351783	464	6	preprintthis	preprintthis	NN
10_1101-2020_10_26_351783	464	7	version	version	NN
10_1101-2020_10_26_351783	464	8	posted	post	VBD
10_1101-2020_10_26_351783	464	9	January	January	NNP
10_1101-2020_10_26_351783	464	10	6	6	CD
10_1101-2020_10_26_351783	464	11	,	,	,
10_1101-2020_10_26_351783	464	12	2021	2021	CD
10_1101-2020_10_26_351783	464	13	.	.	.
10_1101-2020_10_26_351783	464	14	;	;	:
10_1101-2020_10_26_351783	464	15	https://doi.org/10.1101/2020.10.26.351783doi	https://doi.org/10.1101/2020.10.26.351783doi	ADD
10_1101-2020_10_26_351783	464	16	:	:	:
10_1101-2020_10_26_351783	464	17	bioRxiv	biorxiv	VB
10_1101-2020_10_26_351783	464	18	preprint	preprint	NN
10_1101-2020_10_26_351783	464	19	https://doi.org/10.1101/2020.10.26.351783	https://doi.org/10.1101/2020.10.26.351783	NNP
10_1101-2020_10_26_351783	464	20	24	24	CD
10_1101-2020_10_26_351783	464	21	Commun	commun	NN
10_1101-2020_10_26_351783	464	22	.	.	.
10_1101-2020_10_26_351783	465	1	2018;9	2018;9	LS
10_1101-2020_10_26_351783	465	2	.	.	.
10_1101-2020_10_26_351783	466	1	37	37	CD
10_1101-2020_10_26_351783	466	2	.	.	.
10_1101-2020_10_26_351783	467	1	Compston	Compston	NNP
10_1101-2020_10_26_351783	467	2	A	A	NNP
10_1101-2020_10_26_351783	467	3	,	,	,
10_1101-2020_10_26_351783	467	4	Coles	Coles	NNP
10_1101-2020_10_26_351783	467	5	A.	a.	NN
10_1101-2020_10_26_351783	468	1	Multiple	multiple	JJ
10_1101-2020_10_26_351783	468	2	sclerosis	sclerosis	NN
10_1101-2020_10_26_351783	468	3	.	.	.
10_1101-2020_10_26_351783	469	1	Lancet	Lancet	NNP
10_1101-2020_10_26_351783	469	2	[	[	-LRB-
10_1101-2020_10_26_351783	469	3	Internet	internet	NN
10_1101-2020_10_26_351783	469	4	]	]	-RRB-
10_1101-2020_10_26_351783	469	5	.	.	.
10_1101-2020_10_26_351783	470	1	Elsevier	Elsevier	NNP
10_1101-2020_10_26_351783	470	2	Ltd	Ltd	NNP
10_1101-2020_10_26_351783	470	3	;	;	:
10_1101-2020_10_26_351783	470	4	2008;372:1502–17	2008;372:1502–17	CD
10_1101-2020_10_26_351783	470	5	.	.	.
10_1101-2020_10_26_351783	471	1	Available	available	JJ
10_1101-2020_10_26_351783	471	2	from	from	IN
10_1101-2020_10_26_351783	471	3	:	:	:
10_1101-2020_10_26_351783	471	4	http://dx.doi.org/10.1016/S0140-6736(08)61620-7	http://dx.doi.org/10.1016/S0140-6736(08)61620-7	NNP
10_1101-2020_10_26_351783	471	5	38	38	CD
10_1101-2020_10_26_351783	471	6	.	.	.
10_1101-2020_10_26_351783	472	1	Jelcic	Jelcic	NNP
10_1101-2020_10_26_351783	472	2	I	-PRON-	PRP
10_1101-2020_10_26_351783	472	3	,	,	,
10_1101-2020_10_26_351783	472	4	Al	Al	NNP
10_1101-2020_10_26_351783	472	5	Nimer	Nimer	NNP
10_1101-2020_10_26_351783	472	6	F	F	NNP
10_1101-2020_10_26_351783	472	7	,	,	,
10_1101-2020_10_26_351783	472	8	Wang	Wang	NNP
10_1101-2020_10_26_351783	472	9	J	J	NNP
10_1101-2020_10_26_351783	472	10	,	,	,
10_1101-2020_10_26_351783	472	11	Lentsch	Lentsch	NNP
10_1101-2020_10_26_351783	472	12	V	V	NNP
10_1101-2020_10_26_351783	472	13	,	,	,
10_1101-2020_10_26_351783	472	14	Planas	Planas	NNP
10_1101-2020_10_26_351783	472	15	R	R	NNP
10_1101-2020_10_26_351783	472	16	,	,	,
10_1101-2020_10_26_351783	472	17	Jelcic	Jelcic	NNP
10_1101-2020_10_26_351783	472	18	I	I	NNP
10_1101-2020_10_26_351783	472	19	,	,	,
10_1101-2020_10_26_351783	472	20	et	et	NNP
10_1101-2020_10_26_351783	472	21	al	al	NNP
10_1101-2020_10_26_351783	472	22	.	.	.
10_1101-2020_10_26_351783	473	1	Memory	memory	NN
10_1101-2020_10_26_351783	473	2	B	B	NNP
10_1101-2020_10_26_351783	473	3	Cells	cell	NNS
10_1101-2020_10_26_351783	473	4	Activate	activate	VBP
10_1101-2020_10_26_351783	473	5	Brain-	Brain-	NNP
10_1101-2020_10_26_351783	473	6	Homing	Homing	NNP
10_1101-2020_10_26_351783	473	7	,	,	,
10_1101-2020_10_26_351783	473	8	Autoreactive	Autoreactive	NNP
10_1101-2020_10_26_351783	473	9	CD4	cd4	NN
10_1101-2020_10_26_351783	473	10	+	+	SYM
10_1101-2020_10_26_351783	473	11	T	t	NN
10_1101-2020_10_26_351783	473	12	Cells	cell	NNS
10_1101-2020_10_26_351783	473	13	in	in	IN
10_1101-2020_10_26_351783	473	14	Multiple	Multiple	NNP
10_1101-2020_10_26_351783	473	15	Sclerosis	Sclerosis	NNP
10_1101-2020_10_26_351783	473	16	.	.	.
10_1101-2020_10_26_351783	474	1	Cell	cell	NN
10_1101-2020_10_26_351783	474	2	.	.	.
10_1101-2020_10_26_351783	475	1	2018;175:85	2018;175:85	LS
10_1101-2020_10_26_351783	475	2	-	-	SYM
10_1101-2020_10_26_351783	475	3	100.e23	100.e23	NNP
10_1101-2020_10_26_351783	475	4	.	.	.
10_1101-2020_10_26_351783	476	1	39	39	CD
10_1101-2020_10_26_351783	476	2	.	.	.
10_1101-2020_10_26_351783	477	1	Lange	Lange	NNP
10_1101-2020_10_26_351783	477	2	C	C	NNP
10_1101-2020_10_26_351783	477	3	,	,	,
10_1101-2020_10_26_351783	477	4	Storkebaum	Storkebaum	NNP
10_1101-2020_10_26_351783	477	5	E	E	NNP
10_1101-2020_10_26_351783	477	6	,	,	,
10_1101-2020_10_26_351783	477	7	De	De	NNP
10_1101-2020_10_26_351783	477	8	Almodóvar	Almodóvar	NNP
10_1101-2020_10_26_351783	477	9	CR	CR	NNP
10_1101-2020_10_26_351783	477	10	,	,	,
10_1101-2020_10_26_351783	477	11	Dewerchin	Dewerchin	NNP
10_1101-2020_10_26_351783	477	12	M	M	NNP
10_1101-2020_10_26_351783	477	13	,	,	,
10_1101-2020_10_26_351783	477	14	Carmeliet	Carmeliet	NNP
10_1101-2020_10_26_351783	477	15	P.	P.	NNP
10_1101-2020_10_26_351783	477	16	Vascular	vascular	JJ
10_1101-2020_10_26_351783	477	17	endothelial	endothelial	JJ
10_1101-2020_10_26_351783	477	18	growth	growth	NN
10_1101-2020_10_26_351783	477	19	factor	factor	NN
10_1101-2020_10_26_351783	477	20	:	:	:
10_1101-2020_10_26_351783	477	21	A	a	DT
10_1101-2020_10_26_351783	477	22	neurovascular	neurovascular	JJ
10_1101-2020_10_26_351783	477	23	target	target	NN
10_1101-2020_10_26_351783	477	24	in	in	IN
10_1101-2020_10_26_351783	477	25	neurological	neurological	JJ
10_1101-2020_10_26_351783	477	26	diseases	disease	NNS
10_1101-2020_10_26_351783	477	27	.	.	.
10_1101-2020_10_26_351783	478	1	Nat	Nat	NNP
10_1101-2020_10_26_351783	478	2	Rev	Rev	NNP
10_1101-2020_10_26_351783	478	3	Neurol	Neurol	NNP
10_1101-2020_10_26_351783	478	4	[	[	-LRB-
10_1101-2020_10_26_351783	478	5	Internet	internet	NN
10_1101-2020_10_26_351783	478	6	]	]	-RRB-
10_1101-2020_10_26_351783	478	7	.	.	.
10_1101-2020_10_26_351783	479	1	Nature	Nature	NNP
10_1101-2020_10_26_351783	479	2	Publishing	Publishing	NNP
10_1101-2020_10_26_351783	479	3	Group	Group	NNP
10_1101-2020_10_26_351783	479	4	;	;	:
10_1101-2020_10_26_351783	479	5	2016;12:439–54	2016;12:439–54	NNP
10_1101-2020_10_26_351783	479	6	.	.	.
10_1101-2020_10_26_351783	480	1	Available	available	JJ
10_1101-2020_10_26_351783	480	2	from	from	IN
10_1101-2020_10_26_351783	480	3	:	:	:
10_1101-2020_10_26_351783	480	4	http://dx.doi.org/10.1038/nrneurol.2016.88	http://dx.doi.org/10.1038/nrneurol.2016.88	NNP
10_1101-2020_10_26_351783	480	5	(	(	-LRB-
10_1101-2020_10_26_351783	480	6	which	which	WDT
10_1101-2020_10_26_351783	480	7	was	be	VBD
10_1101-2020_10_26_351783	480	8	not	not	RB
10_1101-2020_10_26_351783	480	9	certified	certify	VBN
10_1101-2020_10_26_351783	480	10	by	by	IN
10_1101-2020_10_26_351783	480	11	peer	peer	NN
10_1101-2020_10_26_351783	480	12	review	review	NN
10_1101-2020_10_26_351783	480	13	)	)	-RRB-
10_1101-2020_10_26_351783	480	14	is	be	VBZ
10_1101-2020_10_26_351783	480	15	the	the	DT
10_1101-2020_10_26_351783	480	16	author	author	NN
10_1101-2020_10_26_351783	480	17	/	/	SYM
10_1101-2020_10_26_351783	480	18	funder	funder	NN
10_1101-2020_10_26_351783	480	19	.	.	.
10_1101-2020_10_26_351783	481	1	All	all	DT
10_1101-2020_10_26_351783	481	2	rights	right	NNS
10_1101-2020_10_26_351783	481	3	reserved	reserve	VBD
10_1101-2020_10_26_351783	481	4	.	.	.
10_1101-2020_10_26_351783	482	1	No	no	DT
10_1101-2020_10_26_351783	482	2	reuse	reuse	NN
10_1101-2020_10_26_351783	482	3	allowed	allow	VBN
10_1101-2020_10_26_351783	482	4	without	without	IN
10_1101-2020_10_26_351783	482	5	permission	permission	NN
10_1101-2020_10_26_351783	482	6	.	.	.
10_1101-2020_10_26_351783	483	1	The	the	DT
10_1101-2020_10_26_351783	483	2	copyright	copyright	NN
10_1101-2020_10_26_351783	483	3	holder	holder	NN
10_1101-2020_10_26_351783	483	4	for	for	IN
10_1101-2020_10_26_351783	483	5	this	this	DT
10_1101-2020_10_26_351783	483	6	preprintthis	preprintthis	NN
10_1101-2020_10_26_351783	483	7	version	version	NN
10_1101-2020_10_26_351783	483	8	posted	post	VBD
10_1101-2020_10_26_351783	483	9	January	January	NNP
10_1101-2020_10_26_351783	483	10	6	6	CD
10_1101-2020_10_26_351783	483	11	,	,	,
10_1101-2020_10_26_351783	483	12	2021	2021	CD
10_1101-2020_10_26_351783	483	13	.	.	.
10_1101-2020_10_26_351783	483	14	;	;	:
10_1101-2020_10_26_351783	483	15	https://doi.org/10.1101/2020.10.26.351783doi	https://doi.org/10.1101/2020.10.26.351783doi	ADD
10_1101-2020_10_26_351783	483	16	:	:	:
10_1101-2020_10_26_351783	483	17	bioRxiv	biorxiv	VB
10_1101-2020_10_26_351783	483	18	preprint	preprint	NN
10_1101-2020_10_26_351783	483	19	https://doi.org/10.1101/2020.10.26.351783	https://doi.org/10.1101/2020.10.26.351783	CD
10_1101-2020_10_26_351783	483	20	Figures	figure	NNS
10_1101-2020_10_26_351783	483	21	:	:	:
10_1101-2020_10_26_351783	483	22	Figure	figure	NN
10_1101-2020_10_26_351783	483	23	1	1	CD
10_1101-2020_10_26_351783	483	24	.	.	.
10_1101-2020_10_26_351783	484	1	Overview	overview	NN
10_1101-2020_10_26_351783	484	2	of	of	IN
10_1101-2020_10_26_351783	484	3	the	the	DT
10_1101-2020_10_26_351783	484	4	benchmark	benchmark	NN
10_1101-2020_10_26_351783	484	5	assessment	assessment	NN
10_1101-2020_10_26_351783	484	6	of	of	IN
10_1101-2020_10_26_351783	484	7	disease	disease	NNP
10_1101-2020_10_26_351783	484	8	modules	module	NNS
10_1101-2020_10_26_351783	484	9	and	and	CC
10_1101-2020_10_26_351783	484	10	the	the	DT
10_1101-2020_10_26_351783	484	11	integration	integration	NN
10_1101-2020_10_26_351783	484	12	workflow	workflow	NN
10_1101-2020_10_26_351783	484	13	for	for	IN
10_1101-2020_10_26_351783	484	14	MS	MS	NNP
10_1101-2020_10_26_351783	484	15	.	.	.
10_1101-2020_10_26_351783	484	16	(	(	-LRB-
10_1101-2020_10_26_351783	484	17	a	a	LS
10_1101-2020_10_26_351783	484	18	)	)	-RRB-
10_1101-2020_10_26_351783	484	19	Transcriptomic	transcriptomic	JJ
10_1101-2020_10_26_351783	484	20	and	and	CC
10_1101-2020_10_26_351783	484	21	methylomic	methylomic	JJ
10_1101-2020_10_26_351783	484	22	datasets	dataset	NNS
10_1101-2020_10_26_351783	484	23	from	from	IN
10_1101-2020_10_26_351783	484	24	19	19	CD
10_1101-2020_10_26_351783	484	25	different	different	JJ
10_1101-2020_10_26_351783	484	26	diseases	disease	NNS
10_1101-2020_10_26_351783	484	27	were	be	VBD
10_1101-2020_10_26_351783	484	28	used	use	VBN
10_1101-2020_10_26_351783	484	29	as	as	IN
10_1101-2020_10_26_351783	484	30	inputs	input	NNS
10_1101-2020_10_26_351783	484	31	for	for	IN
10_1101-2020_10_26_351783	484	32	eight	eight	CD
10_1101-2020_10_26_351783	484	33	MODifieR	MODifieR	NNP
10_1101-2020_10_26_351783	484	34	module	module	JJ
10_1101-2020_10_26_351783	484	35	identification	identification	NN
10_1101-2020_10_26_351783	484	36	methods	method	NNS
10_1101-2020_10_26_351783	484	37	.	.	.
10_1101-2020_10_26_351783	485	1	The	the	DT
10_1101-2020_10_26_351783	485	2	resulting	result	VBG
10_1101-2020_10_26_351783	485	3	single	single	JJ
10_1101-2020_10_26_351783	485	4	-	-	HYPH
10_1101-2020_10_26_351783	485	5	omic	omic	JJ
10_1101-2020_10_26_351783	485	6	disease	disease	NN
10_1101-2020_10_26_351783	485	7	modules	module	NNS
10_1101-2020_10_26_351783	485	8	(	(	-LRB-
10_1101-2020_10_26_351783	485	9	n	n	NNP
10_1101-2020_10_26_351783	485	10	=	=	SYM
10_1101-2020_10_26_351783	485	11	(	(	-LRB-
10_1101-2020_10_26_351783	485	12	which	which	WDT
10_1101-2020_10_26_351783	485	13	was	be	VBD
10_1101-2020_10_26_351783	485	14	not	not	RB
10_1101-2020_10_26_351783	485	15	certified	certify	VBN
10_1101-2020_10_26_351783	485	16	by	by	IN
10_1101-2020_10_26_351783	485	17	peer	peer	NN
10_1101-2020_10_26_351783	485	18	review	review	NN
10_1101-2020_10_26_351783	485	19	)	)	-RRB-
10_1101-2020_10_26_351783	485	20	is	be	VBZ
10_1101-2020_10_26_351783	485	21	the	the	DT
10_1101-2020_10_26_351783	485	22	author	author	NN
10_1101-2020_10_26_351783	485	23	/	/	SYM
10_1101-2020_10_26_351783	485	24	funder	funder	NN
10_1101-2020_10_26_351783	485	25	.	.	.
10_1101-2020_10_26_351783	486	1	All	all	DT
10_1101-2020_10_26_351783	486	2	rights	right	NNS
10_1101-2020_10_26_351783	486	3	reserved	reserve	VBD
10_1101-2020_10_26_351783	486	4	.	.	.
10_1101-2020_10_26_351783	487	1	No	no	DT
10_1101-2020_10_26_351783	487	2	reuse	reuse	NN
10_1101-2020_10_26_351783	487	3	allowed	allow	VBN
10_1101-2020_10_26_351783	487	4	without	without	IN
10_1101-2020_10_26_351783	487	5	permission	permission	NN
10_1101-2020_10_26_351783	487	6	.	.	.
10_1101-2020_10_26_351783	488	1	The	the	DT
10_1101-2020_10_26_351783	488	2	copyright	copyright	NN
10_1101-2020_10_26_351783	488	3	holder	holder	NN
10_1101-2020_10_26_351783	488	4	for	for	IN
10_1101-2020_10_26_351783	488	5	this	this	DT
10_1101-2020_10_26_351783	488	6	preprintthis	preprintthis	NN
10_1101-2020_10_26_351783	488	7	version	version	NN
10_1101-2020_10_26_351783	488	8	posted	post	VBD
10_1101-2020_10_26_351783	488	9	January	January	NNP
10_1101-2020_10_26_351783	488	10	6	6	CD
10_1101-2020_10_26_351783	488	11	,	,	,
10_1101-2020_10_26_351783	488	12	2021	2021	CD
10_1101-2020_10_26_351783	488	13	.	.	.
10_1101-2020_10_26_351783	488	14	;	;	:
10_1101-2020_10_26_351783	488	15	https://doi.org/10.1101/2020.10.26.351783doi	https://doi.org/10.1101/2020.10.26.351783doi	ADD
10_1101-2020_10_26_351783	488	16	:	:	:
10_1101-2020_10_26_351783	488	17	bioRxiv	biorxiv	VB
10_1101-2020_10_26_351783	488	18	preprint	preprint	NN
10_1101-2020_10_26_351783	488	19	https://doi.org/10.1101/2020.10.26.351783	https://doi.org/10.1101/2020.10.26.351783	NNP
10_1101-2020_10_26_351783	488	20	26	26	CD
10_1101-2020_10_26_351783	488	21	456	456	CD
10_1101-2020_10_26_351783	488	22	)	)	-RRB-
10_1101-2020_10_26_351783	488	23	were	be	VBD
10_1101-2020_10_26_351783	488	24	independently	independently	RB
10_1101-2020_10_26_351783	488	25	assessed	assess	VBN
10_1101-2020_10_26_351783	488	26	by	by	IN
10_1101-2020_10_26_351783	488	27	GWAS	GWAS	NNP
10_1101-2020_10_26_351783	488	28	enrichment	enrichment	NN
10_1101-2020_10_26_351783	488	29	analysis	analysis	NN
10_1101-2020_10_26_351783	488	30	of	of	IN
10_1101-2020_10_26_351783	488	31	the	the	DT
10_1101-2020_10_26_351783	488	32	same	same	JJ
10_1101-2020_10_26_351783	488	33	disease	disease	NN
10_1101-2020_10_26_351783	488	34	using	use	VBG
10_1101-2020_10_26_351783	488	35	Pascal	pascal	JJ
10_1101-2020_10_26_351783	488	36	module	module	NN
10_1101-2020_10_26_351783	488	37	scoring	score	VBG
10_1101-2020_10_26_351783	488	38	.	.	.
10_1101-2020_10_26_351783	489	1	MODifieR	MODifieR	NNP
10_1101-2020_10_26_351783	489	2	methods	method	NNS
10_1101-2020_10_26_351783	489	3	were	be	VBD
10_1101-2020_10_26_351783	489	4	evaluated	evaluate	VBN
10_1101-2020_10_26_351783	489	5	by	by	IN
10_1101-2020_10_26_351783	489	6	the	the	DT
10_1101-2020_10_26_351783	489	7	combined	combine	VBN
10_1101-2020_10_26_351783	489	8	enrichment	enrichment	NN
10_1101-2020_10_26_351783	489	9	score	score	NN
10_1101-2020_10_26_351783	489	10	of	of	IN
10_1101-2020_10_26_351783	489	11	their	-PRON-	PRP$
10_1101-2020_10_26_351783	489	12	respective	respective	JJ
10_1101-2020_10_26_351783	489	13	disease	disease	NN
10_1101-2020_10_26_351783	489	14	modules	module	NNS
10_1101-2020_10_26_351783	489	15	.	.	.
10_1101-2020_10_26_351783	490	1	(	(	-LRB-
10_1101-2020_10_26_351783	490	2	b	b	LS
10_1101-2020_10_26_351783	490	3	)	)	-RRB-
10_1101-2020_10_26_351783	490	4	Multi	multi	JJ
10_1101-2020_10_26_351783	490	5	-	-	JJ
10_1101-2020_10_26_351783	490	6	omic	omic	JJ
10_1101-2020_10_26_351783	490	7	integrative	integrative	JJ
10_1101-2020_10_26_351783	490	8	workflow	workflow	NN
10_1101-2020_10_26_351783	490	9	for	for	IN
10_1101-2020_10_26_351783	490	10	multiple	multiple	JJ
10_1101-2020_10_26_351783	490	11	sclerosis	sclerosis	NN
10_1101-2020_10_26_351783	490	12	(	(	-LRB-
10_1101-2020_10_26_351783	490	13	MS)-	MS)-	NNP
10_1101-2020_10_26_351783	490	14	associated	associate	VBN
10_1101-2020_10_26_351783	490	15	modules	module	NNS
10_1101-2020_10_26_351783	490	16	.	.	.
10_1101-2020_10_26_351783	491	1	Data	datum	NNS
10_1101-2020_10_26_351783	491	2	from	from	IN
10_1101-2020_10_26_351783	491	3	20	20	CD
10_1101-2020_10_26_351783	491	4	case	case	NN
10_1101-2020_10_26_351783	491	5	-	-	HYPH
10_1101-2020_10_26_351783	491	6	control	control	NN
10_1101-2020_10_26_351783	491	7	comparisons	comparison	NNS
10_1101-2020_10_26_351783	491	8	were	be	VBD
10_1101-2020_10_26_351783	491	9	used	use	VBN
10_1101-2020_10_26_351783	491	10	as	as	IN
10_1101-2020_10_26_351783	491	11	input	input	NN
10_1101-2020_10_26_351783	491	12	for	for	IN
10_1101-2020_10_26_351783	491	13	module	module	JJ
10_1101-2020_10_26_351783	491	14	detection	detection	NN
10_1101-2020_10_26_351783	491	15	with	with	IN
10_1101-2020_10_26_351783	491	16	MODifieR	MODifieR	NNP
10_1101-2020_10_26_351783	491	17	methods	method	NNS
10_1101-2020_10_26_351783	491	18	.	.	.
10_1101-2020_10_26_351783	492	1	Clique	Clique	NNP
10_1101-2020_10_26_351783	492	2	SuM	SuM	NNP
10_1101-2020_10_26_351783	492	3	modules	module	NNS
10_1101-2020_10_26_351783	492	4	presented	present	VBD
10_1101-2020_10_26_351783	492	5	the	the	DT
10_1101-2020_10_26_351783	492	6	highest	high	JJS
10_1101-2020_10_26_351783	492	7	GWAS	GWAS	NNP
10_1101-2020_10_26_351783	492	8	enrichment	enrichment	NN
10_1101-2020_10_26_351783	492	9	score	score	NN
10_1101-2020_10_26_351783	492	10	and	and	CC
10_1101-2020_10_26_351783	492	11	were	be	VBD
10_1101-2020_10_26_351783	492	12	therefore	therefore	RB
10_1101-2020_10_26_351783	492	13	used	use	VBN
10_1101-2020_10_26_351783	492	14	to	to	TO
10_1101-2020_10_26_351783	492	15	generate	generate	VB
10_1101-2020_10_26_351783	492	16	single	single	JJ
10_1101-2020_10_26_351783	492	17	-	-	HYPH
10_1101-2020_10_26_351783	492	18	omic	omic	JJ
10_1101-2020_10_26_351783	492	19	consensus	consensus	NN
10_1101-2020_10_26_351783	492	20	modules	module	NNS
10_1101-2020_10_26_351783	492	21	.	.	.
10_1101-2020_10_26_351783	493	1	The	the	DT
10_1101-2020_10_26_351783	493	2	intersection	intersection	NN
10_1101-2020_10_26_351783	493	3	of	of	IN
10_1101-2020_10_26_351783	493	4	the	the	DT
10_1101-2020_10_26_351783	493	5	best	good	JJS
10_1101-2020_10_26_351783	493	6	transcriptomic	transcriptomic	JJ
10_1101-2020_10_26_351783	493	7	and	and	CC
10_1101-2020_10_26_351783	493	8	methylomic	methylomic	JJ
10_1101-2020_10_26_351783	493	9	consensus	consensus	NN
10_1101-2020_10_26_351783	493	10	modules	module	NNS
10_1101-2020_10_26_351783	493	11	resulted	result	VBD
10_1101-2020_10_26_351783	493	12	in	in	IN
10_1101-2020_10_26_351783	493	13	an	an	DT
10_1101-2020_10_26_351783	493	14	MS	MS	NNP
10_1101-2020_10_26_351783	493	15	multi	multi	JJ
10_1101-2020_10_26_351783	493	16	-	-	JJ
10_1101-2020_10_26_351783	493	17	omic	omic	JJ
10_1101-2020_10_26_351783	493	18	module	module	NN
10_1101-2020_10_26_351783	493	19	(	(	-LRB-
10_1101-2020_10_26_351783	493	20	n	n	NNP
10_1101-2020_10_26_351783	493	21	=	=	SYM
10_1101-2020_10_26_351783	493	22	220	220	CD
10_1101-2020_10_26_351783	493	23	genes	gene	NNS
10_1101-2020_10_26_351783	493	24	)	)	-RRB-
10_1101-2020_10_26_351783	493	25	with	with	IN
10_1101-2020_10_26_351783	493	26	the	the	DT
10_1101-2020_10_26_351783	493	27	highest	high	JJS
10_1101-2020_10_26_351783	493	28	GWAS	GWAS	NNP
10_1101-2020_10_26_351783	493	29	enrichment	enrichment	NN
10_1101-2020_10_26_351783	493	30	,	,	,
10_1101-2020_10_26_351783	493	31	which	which	WDT
10_1101-2020_10_26_351783	493	32	was	be	VBD
10_1101-2020_10_26_351783	493	33	independently	independently	RB
10_1101-2020_10_26_351783	493	34	found	find	VBN
10_1101-2020_10_26_351783	493	35	to	to	TO
10_1101-2020_10_26_351783	493	36	be	be	VB
10_1101-2020_10_26_351783	493	37	enriched	enrich	VBN
10_1101-2020_10_26_351783	493	38	for	for	IN
10_1101-2020_10_26_351783	493	39	genes	gene	NNS
10_1101-2020_10_26_351783	493	40	associated	associate	VBN
10_1101-2020_10_26_351783	493	41	with	with	IN
10_1101-2020_10_26_351783	493	42	five	five	CD
10_1101-2020_10_26_351783	493	43	known	know	VBN
10_1101-2020_10_26_351783	493	44	lifestyle	lifestyle	NN
10_1101-2020_10_26_351783	493	45	MS	MS	NNP
10_1101-2020_10_26_351783	493	46	risk	risk	NN
10_1101-2020_10_26_351783	493	47	factors	factor	NNS
10_1101-2020_10_26_351783	493	48	using	use	VBG
10_1101-2020_10_26_351783	493	49	public	public	JJ
10_1101-2020_10_26_351783	493	50	omics	omic	NNS
10_1101-2020_10_26_351783	493	51	data	datum	NNS
10_1101-2020_10_26_351783	493	52	from	from	IN
10_1101-2020_10_26_351783	493	53	healthy	healthy	JJ
10_1101-2020_10_26_351783	493	54	individuals	individual	NNS
10_1101-2020_10_26_351783	493	55	.	.	.
10_1101-2020_10_26_351783	494	1	(	(	-LRB-
10_1101-2020_10_26_351783	494	2	which	which	WDT
10_1101-2020_10_26_351783	494	3	was	be	VBD
10_1101-2020_10_26_351783	494	4	not	not	RB
10_1101-2020_10_26_351783	494	5	certified	certify	VBN
10_1101-2020_10_26_351783	494	6	by	by	IN
10_1101-2020_10_26_351783	494	7	peer	peer	NN
10_1101-2020_10_26_351783	494	8	review	review	NN
10_1101-2020_10_26_351783	494	9	)	)	-RRB-
10_1101-2020_10_26_351783	494	10	is	be	VBZ
10_1101-2020_10_26_351783	494	11	the	the	DT
10_1101-2020_10_26_351783	494	12	author	author	NN
10_1101-2020_10_26_351783	494	13	/	/	SYM
10_1101-2020_10_26_351783	494	14	funder	funder	NN
10_1101-2020_10_26_351783	494	15	.	.	.
10_1101-2020_10_26_351783	495	1	All	all	DT
10_1101-2020_10_26_351783	495	2	rights	right	NNS
10_1101-2020_10_26_351783	495	3	reserved	reserve	VBD
10_1101-2020_10_26_351783	495	4	.	.	.
10_1101-2020_10_26_351783	496	1	No	no	DT
10_1101-2020_10_26_351783	496	2	reuse	reuse	NN
10_1101-2020_10_26_351783	496	3	allowed	allow	VBN
10_1101-2020_10_26_351783	496	4	without	without	IN
10_1101-2020_10_26_351783	496	5	permission	permission	NN
10_1101-2020_10_26_351783	496	6	.	.	.
10_1101-2020_10_26_351783	497	1	The	the	DT
10_1101-2020_10_26_351783	497	2	copyright	copyright	NN
10_1101-2020_10_26_351783	497	3	holder	holder	NN
10_1101-2020_10_26_351783	497	4	for	for	IN
10_1101-2020_10_26_351783	497	5	this	this	DT
10_1101-2020_10_26_351783	497	6	preprintthis	preprintthis	NN
10_1101-2020_10_26_351783	497	7	version	version	NN
10_1101-2020_10_26_351783	497	8	posted	post	VBD
10_1101-2020_10_26_351783	497	9	January	January	NNP
10_1101-2020_10_26_351783	497	10	6	6	CD
10_1101-2020_10_26_351783	497	11	,	,	,
10_1101-2020_10_26_351783	497	12	2021	2021	CD
10_1101-2020_10_26_351783	497	13	.	.	.
10_1101-2020_10_26_351783	497	14	;	;	:
10_1101-2020_10_26_351783	497	15	https://doi.org/10.1101/2020.10.26.351783doi	https://doi.org/10.1101/2020.10.26.351783doi	ADD
10_1101-2020_10_26_351783	497	16	:	:	:
10_1101-2020_10_26_351783	497	17	bioRxiv	biorxiv	VB
10_1101-2020_10_26_351783	497	18	preprint	preprint	NN
10_1101-2020_10_26_351783	497	19	https://doi.org/10.1101/2020.10.26.351783	https://doi.org/10.1101/2020.10.26.351783	NNP
10_1101-2020_10_26_351783	497	20	Figure	figure	NN
10_1101-2020_10_26_351783	497	21	2	2	CD
10_1101-2020_10_26_351783	497	22	.	.	.
10_1101-2020_10_26_351783	498	1	Genomic	genomic	JJ
10_1101-2020_10_26_351783	498	2	concordance	concordance	NN
10_1101-2020_10_26_351783	498	3	of	of	IN
10_1101-2020_10_26_351783	498	4	MODifieR	MODifieR	NNP
10_1101-2020_10_26_351783	498	5	modules	module	NNS
10_1101-2020_10_26_351783	498	6	on	on	IN
10_1101-2020_10_26_351783	498	7	transcriptomic	transcriptomic	JJ
10_1101-2020_10_26_351783	498	8	datasets	dataset	NNS
10_1101-2020_10_26_351783	498	9	.	.	.
10_1101-2020_10_26_351783	499	1	(	(	-LRB-
10_1101-2020_10_26_351783	499	2	a	a	DT
10_1101-2020_10_26_351783	499	3	)	)	-RRB-
10_1101-2020_10_26_351783	499	4	Heatmap	Heatmap	NNP
10_1101-2020_10_26_351783	499	5	of	of	IN
10_1101-2020_10_26_351783	499	6	PASCAL	PASCAL	NNP
10_1101-2020_10_26_351783	499	7	p	p	NN
10_1101-2020_10_26_351783	499	8	-	-	HYPH
10_1101-2020_10_26_351783	499	9	values	value	NNS
10_1101-2020_10_26_351783	499	10	for	for	IN
10_1101-2020_10_26_351783	499	11	eight	eight	CD
10_1101-2020_10_26_351783	499	12	single	single	JJ
10_1101-2020_10_26_351783	499	13	-	-	HYPH
10_1101-2020_10_26_351783	499	14	method	method	NN
10_1101-2020_10_26_351783	499	15	and	and	CC
10_1101-2020_10_26_351783	499	16	eight	eight	CD
10_1101-2020_10_26_351783	499	17	consensus	consensus	NN
10_1101-2020_10_26_351783	499	18	MODifieR	MODifieR	NNP
10_1101-2020_10_26_351783	499	19	modules	modules	NNP
10_1101-2020_10_26_351783	499	20	,	,	,
10_1101-2020_10_26_351783	499	21	identified	identify	VBN
10_1101-2020_10_26_351783	499	22	for	for	IN
10_1101-2020_10_26_351783	499	23	47	47	CD
10_1101-2020_10_26_351783	499	24	publicly	publicly	RB
10_1101-2020_10_26_351783	499	25	available	available	JJ
10_1101-2020_10_26_351783	499	26	transcriptomic	transcriptomic	JJ
10_1101-2020_10_26_351783	499	27	datasets	dataset	NNS
10_1101-2020_10_26_351783	499	28	.	.	.
10_1101-2020_10_26_351783	500	1	Module	module	JJ
10_1101-2020_10_26_351783	500	2	performance	performance	NN
10_1101-2020_10_26_351783	500	3	P	p	NN
10_1101-2020_10_26_351783	500	4	-	-	HYPH
10_1101-2020_10_26_351783	500	5	values	value	NNS
10_1101-2020_10_26_351783	500	6	are	be	VBP
10_1101-2020_10_26_351783	500	7	shown	show	VBN
10_1101-2020_10_26_351783	500	8	in	in	IN
10_1101-2020_10_26_351783	500	9	a	a	DT
10_1101-2020_10_26_351783	500	10	white	white	NN
10_1101-2020_10_26_351783	500	11	to	to	IN
10_1101-2020_10_26_351783	500	12	blue	blue	JJ
10_1101-2020_10_26_351783	500	13	scale	scale	NN
10_1101-2020_10_26_351783	500	14	,	,	,
10_1101-2020_10_26_351783	500	15	where	where	WRB
10_1101-2020_10_26_351783	500	16	any	any	DT
10_1101-2020_10_26_351783	500	17	shade	shade	NN
10_1101-2020_10_26_351783	500	18	of	of	IN
10_1101-2020_10_26_351783	500	19	blue	blue	JJ
10_1101-2020_10_26_351783	500	20	represents	represent	VBZ
10_1101-2020_10_26_351783	500	21	a	a	DT
10_1101-2020_10_26_351783	500	22	significant	significant	JJ
10_1101-2020_10_26_351783	500	23	module	module	NN
10_1101-2020_10_26_351783	500	24	(	(	-LRB-
10_1101-2020_10_26_351783	500	25	<	<	XX
10_1101-2020_10_26_351783	500	26	0.05	0.05	XX
10_1101-2020_10_26_351783	500	27	;	;	:
10_1101-2020_10_26_351783	500	28	the	the	DT
10_1101-2020_10_26_351783	500	29	darker	darker	NN
10_1101-2020_10_26_351783	500	30	,	,	,
10_1101-2020_10_26_351783	500	31	the	the	DT
10_1101-2020_10_26_351783	500	32	more	more	RBR
10_1101-2020_10_26_351783	500	33	significant	significant	JJ
10_1101-2020_10_26_351783	500	34	)	)	-RRB-
10_1101-2020_10_26_351783	500	35	,	,	,
10_1101-2020_10_26_351783	500	36	white	white	NNP
10_1101-2020_10_26_351783	500	37	represents	represent	VBZ
10_1101-2020_10_26_351783	500	38	a	a	DT
10_1101-2020_10_26_351783	500	39	non	non	JJ
10_1101-2020_10_26_351783	500	40	-	-	JJ
10_1101-2020_10_26_351783	500	41	significant	significant	JJ
10_1101-2020_10_26_351783	500	42	module	module	NN
10_1101-2020_10_26_351783	500	43	,	,	,
10_1101-2020_10_26_351783	500	44	and	and	CC
10_1101-2020_10_26_351783	500	45	grey	grey	NNP
10_1101-2020_10_26_351783	500	46	represents	represent	VBZ
10_1101-2020_10_26_351783	500	47	a	a	DT
10_1101-2020_10_26_351783	500	48	module	module	NN
10_1101-2020_10_26_351783	500	49	of	of	IN
10_1101-2020_10_26_351783	500	50	size	size	NN
10_1101-2020_10_26_351783	500	51	zero	zero	CD
10_1101-2020_10_26_351783	500	52	.	.	.
10_1101-2020_10_26_351783	501	1	Datasets	dataset	NNS
10_1101-2020_10_26_351783	501	2	are	be	VBP
10_1101-2020_10_26_351783	501	3	classified	classify	VBN
10_1101-2020_10_26_351783	501	4	into	into	IN
10_1101-2020_10_26_351783	501	5	six	six	CD
10_1101-2020_10_26_351783	501	6	disease	disease	NN
10_1101-2020_10_26_351783	501	7	types	type	NNS
10_1101-2020_10_26_351783	501	8	:	:	:
10_1101-2020_10_26_351783	501	9	cardiovascular	cardiovascular	NNP
10_1101-2020_10_26_351783	501	10	(	(	-LRB-
10_1101-2020_10_26_351783	501	11	red	red	NNP
10_1101-2020_10_26_351783	501	12	)	)	-RRB-
10_1101-2020_10_26_351783	501	13	,	,	,
10_1101-2020_10_26_351783	501	14	glycemic	glycemic	NNP
10_1101-2020_10_26_351783	501	15	(	(	-LRB-
10_1101-2020_10_26_351783	501	16	golden	golden	NNP
10_1101-2020_10_26_351783	501	17	)	)	-RRB-
10_1101-2020_10_26_351783	501	18	,	,	,
10_1101-2020_10_26_351783	501	19	inflammatory	inflammatory	JJ
10_1101-2020_10_26_351783	501	20	(	(	-LRB-
10_1101-2020_10_26_351783	501	21	green	green	JJ
10_1101-2020_10_26_351783	501	22	)	)	-RRB-
10_1101-2020_10_26_351783	501	23	,	,	,
10_1101-2020_10_26_351783	501	24	neurodegenerative	neurodegenerative	JJ
10_1101-2020_10_26_351783	501	25	(	(	-LRB-
10_1101-2020_10_26_351783	501	26	fuchsia	fuchsia	NNP
10_1101-2020_10_26_351783	501	27	)	)	-RRB-
10_1101-2020_10_26_351783	501	28	,	,	,
10_1101-2020_10_26_351783	501	29	psychiatric	psychiatric	JJ
10_1101-2020_10_26_351783	501	30	and	and	CC
10_1101-2020_10_26_351783	501	31	social	social	JJ
10_1101-2020_10_26_351783	501	32	(	(	-LRB-
10_1101-2020_10_26_351783	501	33	pink	pink	JJ
10_1101-2020_10_26_351783	501	34	)	)	-RRB-
10_1101-2020_10_26_351783	501	35	,	,	,
10_1101-2020_10_26_351783	501	36	autoimmune	autoimmune	NNP
10_1101-2020_10_26_351783	501	37	(	(	-LRB-
10_1101-2020_10_26_351783	501	38	dark	dark	JJ
10_1101-2020_10_26_351783	501	39	purple	purple	NNP
10_1101-2020_10_26_351783	501	40	)	)	-RRB-
10_1101-2020_10_26_351783	501	41	,	,	,
10_1101-2020_10_26_351783	501	42	and	and	CC
10_1101-2020_10_26_351783	501	43	others	other	NNS
10_1101-2020_10_26_351783	501	44	(	(	-LRB-
10_1101-2020_10_26_351783	501	45	light	light	JJ
10_1101-2020_10_26_351783	501	46	purple	purple	NN
10_1101-2020_10_26_351783	501	47	)	)	-RRB-
10_1101-2020_10_26_351783	501	48	;	;	:
10_1101-2020_10_26_351783	501	49	and	and	CC
10_1101-2020_10_26_351783	501	50	two	two	CD
10_1101-2020_10_26_351783	501	51	cell	cell	NN
10_1101-2020_10_26_351783	501	52	types	type	NNS
10_1101-2020_10_26_351783	501	53	:	:	:
10_1101-2020_10_26_351783	501	54	blood	blood	NN
10_1101-2020_10_26_351783	501	55	(	(	-LRB-
10_1101-2020_10_26_351783	501	56	maroon	maroon	NNP
10_1101-2020_10_26_351783	501	57	)	)	-RRB-
10_1101-2020_10_26_351783	501	58	,	,	,
10_1101-2020_10_26_351783	501	59	and	and	CC
10_1101-2020_10_26_351783	501	60	others	other	NNS
10_1101-2020_10_26_351783	501	61	(	(	-LRB-
10_1101-2020_10_26_351783	501	62	light	light	JJ
10_1101-2020_10_26_351783	501	63	yellow	yellow	NN
10_1101-2020_10_26_351783	501	64	)	)	-RRB-
10_1101-2020_10_26_351783	501	65	.	.	.
10_1101-2020_10_26_351783	502	1	Datasets	dataset	NNS
10_1101-2020_10_26_351783	502	2	are	be	VBP
10_1101-2020_10_26_351783	502	3	ranked	rank	VBN
10_1101-2020_10_26_351783	502	4	by	by	IN
10_1101-2020_10_26_351783	502	5	meta	meta	JJ
10_1101-2020_10_26_351783	502	6	P	p	NN
10_1101-2020_10_26_351783	502	7	-	-	HYPH
10_1101-2020_10_26_351783	502	8	values	value	NNS
10_1101-2020_10_26_351783	502	9	using	use	VBG
10_1101-2020_10_26_351783	502	10	Fisher	Fisher	NNP
10_1101-2020_10_26_351783	502	11	’s	’s	POS
10_1101-2020_10_26_351783	502	12	method	method	NN
10_1101-2020_10_26_351783	502	13	of	of	IN
10_1101-2020_10_26_351783	502	14	the	the	DT
10_1101-2020_10_26_351783	502	15	single	single	JJ
10_1101-2020_10_26_351783	502	16	-	-	HYPH
10_1101-2020_10_26_351783	502	17	method	method	NN
10_1101-2020_10_26_351783	502	18	module	module	NN
10_1101-2020_10_26_351783	502	19	P	p	NN
10_1101-2020_10_26_351783	502	20	-	-	HYPH
10_1101-2020_10_26_351783	502	21	values	value	NNS
10_1101-2020_10_26_351783	502	22	across	across	IN
10_1101-2020_10_26_351783	502	23	and	and	CC
10_1101-2020_10_26_351783	502	24	within	within	IN
10_1101-2020_10_26_351783	502	25	their	-PRON-	PRP$
10_1101-2020_10_26_351783	502	26	disease	disease	NN
10_1101-2020_10_26_351783	502	27	types	type	NNS
10_1101-2020_10_26_351783	502	28	(	(	-LRB-
10_1101-2020_10_26_351783	502	29	dataset	dataset	NNP
10_1101-2020_10_26_351783	502	30	score	score	NN
10_1101-2020_10_26_351783	502	31	,	,	,
10_1101-2020_10_26_351783	502	32	bottom	bottom	JJ
10_1101-2020_10_26_351783	502	33	boxplot	boxplot	NN
10_1101-2020_10_26_351783	502	34	)	)	-RRB-
10_1101-2020_10_26_351783	502	35	.	.	.
10_1101-2020_10_26_351783	503	1	MODifieR	MODifieR	NNP
10_1101-2020_10_26_351783	503	2	methods	method	NNS
10_1101-2020_10_26_351783	503	3	are	be	VBP
10_1101-2020_10_26_351783	503	4	organized	organize	VBN
10_1101-2020_10_26_351783	503	5	by	by	IN
10_1101-2020_10_26_351783	503	6	algorithm	algorithm	NNP
10_1101-2020_10_26_351783	503	7	type	type	NN
10_1101-2020_10_26_351783	503	8	:	:	:
10_1101-2020_10_26_351783	503	9	seed	seed	NN
10_1101-2020_10_26_351783	503	10	-	-	HYPH
10_1101-2020_10_26_351783	503	11	based	base	VBN
10_1101-2020_10_26_351783	503	12	(	(	-LRB-
10_1101-2020_10_26_351783	503	13	green	green	NNP
10_1101-2020_10_26_351783	503	14	)	)	-RRB-
10_1101-2020_10_26_351783	503	15	,	,	,
10_1101-2020_10_26_351783	503	16	co	co	NN
10_1101-2020_10_26_351783	503	17	-	-	NN
10_1101-2020_10_26_351783	503	18	expression	expression	NN
10_1101-2020_10_26_351783	503	19	-	-	HYPH
10_1101-2020_10_26_351783	503	20	based	base	VBN
10_1101-2020_10_26_351783	503	21	(	(	-LRB-
10_1101-2020_10_26_351783	503	22	yellow	yellow	NNP
10_1101-2020_10_26_351783	503	23	)	)	-RRB-
10_1101-2020_10_26_351783	503	24	,	,	,
10_1101-2020_10_26_351783	503	25	and	and	CC
10_1101-2020_10_26_351783	503	26	clique	clique	NN
10_1101-2020_10_26_351783	503	27	-	-	HYPH
10_1101-2020_10_26_351783	503	28	based	base	VBN
10_1101-2020_10_26_351783	503	29	(	(	-LRB-
10_1101-2020_10_26_351783	503	30	red	red	NNP
10_1101-2020_10_26_351783	503	31	)	)	-RRB-
10_1101-2020_10_26_351783	503	32	,	,	,
10_1101-2020_10_26_351783	503	33	plus	plus	CC
10_1101-2020_10_26_351783	503	34	the	the	DT
10_1101-2020_10_26_351783	503	35	consensus	consensus	NN
10_1101-2020_10_26_351783	503	36	modules	module	NNS
10_1101-2020_10_26_351783	503	37	(	(	-LRB-
10_1101-2020_10_26_351783	503	38	blue	blue	NNP
10_1101-2020_10_26_351783	503	39	)	)	-RRB-
10_1101-2020_10_26_351783	503	40	.	.	.
10_1101-2020_10_26_351783	504	1	Single	single	JJ
10_1101-2020_10_26_351783	504	2	-	-	HYPH
10_1101-2020_10_26_351783	504	3	methods	method	NNS
10_1101-2020_10_26_351783	504	4	and	and	CC
10_1101-2020_10_26_351783	504	5	consensus	consensus	NN
10_1101-2020_10_26_351783	504	6	were	be	VBD
10_1101-2020_10_26_351783	504	7	scored	score	VBN
10_1101-2020_10_26_351783	504	8	by	by	IN
10_1101-2020_10_26_351783	504	9	meta	meta	JJ
10_1101-2020_10_26_351783	504	10	P	p	NN
10_1101-2020_10_26_351783	504	11	-	-	HYPH
10_1101-2020_10_26_351783	504	12	values	value	NNS
10_1101-2020_10_26_351783	504	13	across	across	IN
10_1101-2020_10_26_351783	504	14	(	(	-LRB-
10_1101-2020_10_26_351783	504	15	which	which	WDT
10_1101-2020_10_26_351783	504	16	was	be	VBD
10_1101-2020_10_26_351783	504	17	not	not	RB
10_1101-2020_10_26_351783	504	18	certified	certify	VBN
10_1101-2020_10_26_351783	504	19	by	by	IN
10_1101-2020_10_26_351783	504	20	peer	peer	NN
10_1101-2020_10_26_351783	504	21	review	review	NN
10_1101-2020_10_26_351783	504	22	)	)	-RRB-
10_1101-2020_10_26_351783	504	23	is	be	VBZ
10_1101-2020_10_26_351783	504	24	the	the	DT
10_1101-2020_10_26_351783	504	25	author	author	NN
10_1101-2020_10_26_351783	504	26	/	/	SYM
10_1101-2020_10_26_351783	504	27	funder	funder	NN
10_1101-2020_10_26_351783	504	28	.	.	.
10_1101-2020_10_26_351783	505	1	All	all	DT
10_1101-2020_10_26_351783	505	2	rights	right	NNS
10_1101-2020_10_26_351783	505	3	reserved	reserve	VBD
10_1101-2020_10_26_351783	505	4	.	.	.
10_1101-2020_10_26_351783	506	1	No	no	DT
10_1101-2020_10_26_351783	506	2	reuse	reuse	NN
10_1101-2020_10_26_351783	506	3	allowed	allow	VBN
10_1101-2020_10_26_351783	506	4	without	without	IN
10_1101-2020_10_26_351783	506	5	permission	permission	NN
10_1101-2020_10_26_351783	506	6	.	.	.
10_1101-2020_10_26_351783	507	1	The	the	DT
10_1101-2020_10_26_351783	507	2	copyright	copyright	NN
10_1101-2020_10_26_351783	507	3	holder	holder	NN
10_1101-2020_10_26_351783	507	4	for	for	IN
10_1101-2020_10_26_351783	507	5	this	this	DT
10_1101-2020_10_26_351783	507	6	preprintthis	preprintthis	NN
10_1101-2020_10_26_351783	507	7	version	version	NN
10_1101-2020_10_26_351783	507	8	posted	post	VBD
10_1101-2020_10_26_351783	507	9	January	January	NNP
10_1101-2020_10_26_351783	507	10	6	6	CD
10_1101-2020_10_26_351783	507	11	,	,	,
10_1101-2020_10_26_351783	507	12	2021	2021	CD
10_1101-2020_10_26_351783	507	13	.	.	.
10_1101-2020_10_26_351783	507	14	;	;	:
10_1101-2020_10_26_351783	507	15	https://doi.org/10.1101/2020.10.26.351783doi	https://doi.org/10.1101/2020.10.26.351783doi	ADD
10_1101-2020_10_26_351783	507	16	:	:	:
10_1101-2020_10_26_351783	507	17	bioRxiv	biorxiv	IN
10_1101-2020_10_26_351783	507	18	preprint	preprint	NN
10_1101-2020_10_26_351783	507	19	https://doi.org/10.1101/2020.10.26.351783	https://doi.org/10.1101/2020.10.26.351783	NNP
10_1101-2020_10_26_351783	507	20	28	28	CD
10_1101-2020_10_26_351783	507	21	datasets	dataset	NNS
10_1101-2020_10_26_351783	507	22	(	(	-LRB-
10_1101-2020_10_26_351783	507	23	method	method	NN
10_1101-2020_10_26_351783	507	24	score	score	NN
10_1101-2020_10_26_351783	507	25	,	,	,
10_1101-2020_10_26_351783	507	26	right	right	JJ
10_1101-2020_10_26_351783	507	27	boxplot	boxplot	NN
10_1101-2020_10_26_351783	507	28	)	)	-RRB-
10_1101-2020_10_26_351783	507	29	.	.	.
10_1101-2020_10_26_351783	508	1	Consensus	Consensus	NNP
10_1101-2020_10_26_351783	508	2	x/8	x/8	NN
10_1101-2020_10_26_351783	508	3	indicates	indicate	VBZ
10_1101-2020_10_26_351783	508	4	that	that	IN
10_1101-2020_10_26_351783	508	5	the	the	DT
10_1101-2020_10_26_351783	508	6	module	module	JJ
10_1101-2020_10_26_351783	508	7	genes	gene	NNS
10_1101-2020_10_26_351783	508	8	are	be	VBP
10_1101-2020_10_26_351783	508	9	found	find	VBN
10_1101-2020_10_26_351783	508	10	in	in	IN
10_1101-2020_10_26_351783	508	11	at	at	IN
10_1101-2020_10_26_351783	508	12	least	least	JJS
10_1101-2020_10_26_351783	508	13	x	x	DT
10_1101-2020_10_26_351783	508	14	methods	method	NNS
10_1101-2020_10_26_351783	508	15	out	out	IN
10_1101-2020_10_26_351783	508	16	of	of	IN
10_1101-2020_10_26_351783	508	17	eight	eight	CD
10_1101-2020_10_26_351783	508	18	.	.	.
10_1101-2020_10_26_351783	509	1	(	(	-LRB-
10_1101-2020_10_26_351783	509	2	b	b	LS
10_1101-2020_10_26_351783	509	3	)	)	-RRB-
10_1101-2020_10_26_351783	509	4	Scatter	scatter	NN
10_1101-2020_10_26_351783	509	5	plot	plot	NN
10_1101-2020_10_26_351783	509	6	showing	show	VBG
10_1101-2020_10_26_351783	509	7	Spearman	spearman	NN
10_1101-2020_10_26_351783	509	8	correlation	correlation	NN
10_1101-2020_10_26_351783	509	9	between	between	IN
10_1101-2020_10_26_351783	509	10	module	module	JJ
10_1101-2020_10_26_351783	509	11	score	score	NN
10_1101-2020_10_26_351783	509	12	and	and	CC
10_1101-2020_10_26_351783	509	13	betweenness	betweenness	JJ
10_1101-2020_10_26_351783	509	14	centrality	centrality	NN
10_1101-2020_10_26_351783	509	15	.	.	.
10_1101-2020_10_26_351783	510	1	Modules	module	NNS
10_1101-2020_10_26_351783	510	2	are	be	VBP
10_1101-2020_10_26_351783	510	3	represented	represent	VBN
10_1101-2020_10_26_351783	510	4	with	with	IN
10_1101-2020_10_26_351783	510	5	a	a	DT
10_1101-2020_10_26_351783	510	6	different	different	JJ
10_1101-2020_10_26_351783	510	7	shape	shape	NN
10_1101-2020_10_26_351783	510	8	depending	depend	VBG
10_1101-2020_10_26_351783	510	9	on	on	IN
10_1101-2020_10_26_351783	510	10	their	-PRON-	PRP$
10_1101-2020_10_26_351783	510	11	method	method	NN
10_1101-2020_10_26_351783	510	12	and	and	CC
10_1101-2020_10_26_351783	510	13	colored	color	VBN
10_1101-2020_10_26_351783	510	14	based	base	VBN
10_1101-2020_10_26_351783	510	15	on	on	IN
10_1101-2020_10_26_351783	510	16	the	the	DT
10_1101-2020_10_26_351783	510	17	disease	disease	NN
10_1101-2020_10_26_351783	510	18	type	type	NN
10_1101-2020_10_26_351783	510	19	.	.	.
10_1101-2020_10_26_351783	511	1	(	(	-LRB-
10_1101-2020_10_26_351783	511	2	c	c	NN
10_1101-2020_10_26_351783	511	3	)	)	-RRB-
10_1101-2020_10_26_351783	511	4	Scatter	scatter	NN
10_1101-2020_10_26_351783	511	5	plot	plot	NN
10_1101-2020_10_26_351783	511	6	showing	show	VBG
10_1101-2020_10_26_351783	511	7	Spearman	spearman	NN
10_1101-2020_10_26_351783	511	8	correlation	correlation	NN
10_1101-2020_10_26_351783	511	9	between	between	IN
10_1101-2020_10_26_351783	511	10	module	module	JJ
10_1101-2020_10_26_351783	511	11	score	score	NN
10_1101-2020_10_26_351783	511	12	and	and	CC
10_1101-2020_10_26_351783	511	13	module	module	JJ
10_1101-2020_10_26_351783	511	14	size	size	NN
10_1101-2020_10_26_351783	511	15	.	.	.
10_1101-2020_10_26_351783	512	1	Modules	module	NNS
10_1101-2020_10_26_351783	512	2	are	be	VBP
10_1101-2020_10_26_351783	512	3	represented	represent	VBN
10_1101-2020_10_26_351783	512	4	with	with	IN
10_1101-2020_10_26_351783	512	5	a	a	DT
10_1101-2020_10_26_351783	512	6	different	different	JJ
10_1101-2020_10_26_351783	512	7	shape	shape	NN
10_1101-2020_10_26_351783	512	8	depending	depend	VBG
10_1101-2020_10_26_351783	512	9	on	on	IN
10_1101-2020_10_26_351783	512	10	their	-PRON-	PRP$
10_1101-2020_10_26_351783	512	11	method	method	NN
10_1101-2020_10_26_351783	512	12	and	and	CC
10_1101-2020_10_26_351783	512	13	colored	color	VBN
10_1101-2020_10_26_351783	512	14	based	base	VBN
10_1101-2020_10_26_351783	512	15	on	on	IN
10_1101-2020_10_26_351783	512	16	the	the	DT
10_1101-2020_10_26_351783	512	17	disease	disease	NN
10_1101-2020_10_26_351783	512	18	type	type	NN
10_1101-2020_10_26_351783	512	19	.	.	.
10_1101-2020_10_26_351783	513	1	(	(	-LRB-
10_1101-2020_10_26_351783	513	2	which	which	WDT
10_1101-2020_10_26_351783	513	3	was	be	VBD
10_1101-2020_10_26_351783	513	4	not	not	RB
10_1101-2020_10_26_351783	513	5	certified	certify	VBN
10_1101-2020_10_26_351783	513	6	by	by	IN
10_1101-2020_10_26_351783	513	7	peer	peer	NN
10_1101-2020_10_26_351783	513	8	review	review	NN
10_1101-2020_10_26_351783	513	9	)	)	-RRB-
10_1101-2020_10_26_351783	513	10	is	be	VBZ
10_1101-2020_10_26_351783	513	11	the	the	DT
10_1101-2020_10_26_351783	513	12	author	author	NN
10_1101-2020_10_26_351783	513	13	/	/	SYM
10_1101-2020_10_26_351783	513	14	funder	funder	NN
10_1101-2020_10_26_351783	513	15	.	.	.
10_1101-2020_10_26_351783	514	1	All	all	DT
10_1101-2020_10_26_351783	514	2	rights	right	NNS
10_1101-2020_10_26_351783	514	3	reserved	reserve	VBD
10_1101-2020_10_26_351783	514	4	.	.	.
10_1101-2020_10_26_351783	515	1	No	no	DT
10_1101-2020_10_26_351783	515	2	reuse	reuse	NN
10_1101-2020_10_26_351783	515	3	allowed	allow	VBN
10_1101-2020_10_26_351783	515	4	without	without	IN
10_1101-2020_10_26_351783	515	5	permission	permission	NN
10_1101-2020_10_26_351783	515	6	.	.	.
10_1101-2020_10_26_351783	516	1	The	the	DT
10_1101-2020_10_26_351783	516	2	copyright	copyright	NN
10_1101-2020_10_26_351783	516	3	holder	holder	NN
10_1101-2020_10_26_351783	516	4	for	for	IN
10_1101-2020_10_26_351783	516	5	this	this	DT
10_1101-2020_10_26_351783	516	6	preprintthis	preprintthis	NN
10_1101-2020_10_26_351783	516	7	version	version	NN
10_1101-2020_10_26_351783	516	8	posted	post	VBD
10_1101-2020_10_26_351783	516	9	January	January	NNP
10_1101-2020_10_26_351783	516	10	6	6	CD
10_1101-2020_10_26_351783	516	11	,	,	,
10_1101-2020_10_26_351783	516	12	2021	2021	CD
10_1101-2020_10_26_351783	516	13	.	.	.
10_1101-2020_10_26_351783	516	14	;	;	:
10_1101-2020_10_26_351783	516	15	https://doi.org/10.1101/2020.10.26.351783doi	https://doi.org/10.1101/2020.10.26.351783doi	ADD
10_1101-2020_10_26_351783	516	16	:	:	:
10_1101-2020_10_26_351783	516	17	bioRxiv	biorxiv	VB
10_1101-2020_10_26_351783	516	18	preprint	preprint	NN
10_1101-2020_10_26_351783	516	19	https://doi.org/10.1101/2020.10.26.351783	https://doi.org/10.1101/2020.10.26.351783	NNP
10_1101-2020_10_26_351783	516	20	Figure	figure	NN
10_1101-2020_10_26_351783	516	21	3	3	CD
10_1101-2020_10_26_351783	516	22	.	.	.
10_1101-2020_10_26_351783	517	1	Genomic	genomic	JJ
10_1101-2020_10_26_351783	517	2	concordance	concordance	NN
10_1101-2020_10_26_351783	517	3	of	of	IN
10_1101-2020_10_26_351783	517	4	MODifieR	MODifieR	NNP
10_1101-2020_10_26_351783	517	5	modules	module	NNS
10_1101-2020_10_26_351783	517	6	on	on	IN
10_1101-2020_10_26_351783	517	7	methylomic	methylomic	JJ
10_1101-2020_10_26_351783	517	8	datasets	dataset	NNS
10_1101-2020_10_26_351783	517	9	.	.	.
10_1101-2020_10_26_351783	518	1	(	(	-LRB-
10_1101-2020_10_26_351783	518	2	a	a	DT
10_1101-2020_10_26_351783	518	3	)	)	-RRB-
10_1101-2020_10_26_351783	518	4	Heatmap	Heatmap	NNP
10_1101-2020_10_26_351783	518	5	of	of	IN
10_1101-2020_10_26_351783	518	6	Pascal	Pascal	NNP
10_1101-2020_10_26_351783	518	7	p	p	NN
10_1101-2020_10_26_351783	518	8	-	-	HYPH
10_1101-2020_10_26_351783	518	9	values	value	NNS
10_1101-2020_10_26_351783	518	10	for	for	IN
10_1101-2020_10_26_351783	518	11	eight	eight	CD
10_1101-2020_10_26_351783	518	12	single	single	JJ
10_1101-2020_10_26_351783	518	13	-	-	HYPH
10_1101-2020_10_26_351783	518	14	method	method	NN
10_1101-2020_10_26_351783	518	15	and	and	CC
10_1101-2020_10_26_351783	518	16	eight	eight	CD
10_1101-2020_10_26_351783	518	17	consensus	consensus	NN
10_1101-2020_10_26_351783	518	18	MODifieR	MODifieR	NNP
10_1101-2020_10_26_351783	518	19	modules	modules	NNP
10_1101-2020_10_26_351783	518	20	,	,	,
10_1101-2020_10_26_351783	518	21	identified	identify	VBN
10_1101-2020_10_26_351783	518	22	for	for	IN
10_1101-2020_10_26_351783	518	23	ten	ten	CD
10_1101-2020_10_26_351783	518	24	publicly	publicly	RB
10_1101-2020_10_26_351783	518	25	available	available	JJ
10_1101-2020_10_26_351783	518	26	methylomic	methylomic	JJ
10_1101-2020_10_26_351783	518	27	datasets	dataset	NNS
10_1101-2020_10_26_351783	518	28	.	.	.
10_1101-2020_10_26_351783	519	1	Module	module	JJ
10_1101-2020_10_26_351783	519	2	performance	performance	NN
10_1101-2020_10_26_351783	519	3	P	p	NN
10_1101-2020_10_26_351783	519	4	-	-	HYPH
10_1101-2020_10_26_351783	519	5	values	value	NNS
10_1101-2020_10_26_351783	519	6	are	be	VBP
10_1101-2020_10_26_351783	519	7	shown	show	VBN
10_1101-2020_10_26_351783	519	8	in	in	IN
10_1101-2020_10_26_351783	519	9	a	a	DT
10_1101-2020_10_26_351783	519	10	white	white	NN
10_1101-2020_10_26_351783	519	11	to	to	IN
10_1101-2020_10_26_351783	519	12	blue	blue	JJ
10_1101-2020_10_26_351783	519	13	scale	scale	NN
10_1101-2020_10_26_351783	519	14	,	,	,
10_1101-2020_10_26_351783	519	15	where	where	WRB
10_1101-2020_10_26_351783	519	16	any	any	DT
10_1101-2020_10_26_351783	519	17	shade	shade	NN
10_1101-2020_10_26_351783	519	18	of	of	IN
10_1101-2020_10_26_351783	519	19	blue	blue	JJ
10_1101-2020_10_26_351783	519	20	represents	represent	VBZ
10_1101-2020_10_26_351783	519	21	a	a	DT
10_1101-2020_10_26_351783	519	22	significant	significant	JJ
10_1101-2020_10_26_351783	519	23	module	module	NN
10_1101-2020_10_26_351783	519	24	(	(	-LRB-
10_1101-2020_10_26_351783	519	25	P	p	NN
10_1101-2020_10_26_351783	519	26	<	<	XX
10_1101-2020_10_26_351783	519	27	0.05	0.05	XX
10_1101-2020_10_26_351783	519	28	;	;	:
10_1101-2020_10_26_351783	519	29	the	the	DT
10_1101-2020_10_26_351783	519	30	darker	darker	NN
10_1101-2020_10_26_351783	519	31	,	,	,
10_1101-2020_10_26_351783	519	32	the	the	DT
10_1101-2020_10_26_351783	519	33	more	more	RBR
10_1101-2020_10_26_351783	519	34	significant	significant	JJ
10_1101-2020_10_26_351783	519	35	)	)	-RRB-
10_1101-2020_10_26_351783	519	36	,	,	,
10_1101-2020_10_26_351783	519	37	white	white	NNP
10_1101-2020_10_26_351783	519	38	represents	represent	VBZ
10_1101-2020_10_26_351783	519	39	a	a	DT
10_1101-2020_10_26_351783	519	40	non	non	JJ
10_1101-2020_10_26_351783	519	41	-	-	JJ
10_1101-2020_10_26_351783	519	42	significant	significant	JJ
10_1101-2020_10_26_351783	519	43	module	module	NN
10_1101-2020_10_26_351783	519	44	,	,	,
10_1101-2020_10_26_351783	519	45	and	and	CC
10_1101-2020_10_26_351783	519	46	grey	grey	NNP
10_1101-2020_10_26_351783	519	47	represents	represent	VBZ
10_1101-2020_10_26_351783	519	48	a	a	DT
10_1101-2020_10_26_351783	519	49	module	module	NN
10_1101-2020_10_26_351783	519	50	of	of	IN
10_1101-2020_10_26_351783	519	51	size	size	NN
10_1101-2020_10_26_351783	519	52	zero	zero	CD
10_1101-2020_10_26_351783	519	53	.	.	.
10_1101-2020_10_26_351783	520	1	Datasets	dataset	NNS
10_1101-2020_10_26_351783	520	2	are	be	VBP
10_1101-2020_10_26_351783	520	3	classified	classify	VBN
10_1101-2020_10_26_351783	520	4	into	into	IN
10_1101-2020_10_26_351783	520	5	two	two	CD
10_1101-2020_10_26_351783	520	6	disease	disease	NN
10_1101-2020_10_26_351783	520	7	types	type	NNS
10_1101-2020_10_26_351783	520	8	:	:	:
10_1101-2020_10_26_351783	520	9	glycemic	glycemic	NNP
10_1101-2020_10_26_351783	520	10	(	(	-LRB-
10_1101-2020_10_26_351783	520	11	golden	golden	NNP
10_1101-2020_10_26_351783	520	12	)	)	-RRB-
10_1101-2020_10_26_351783	520	13	,	,	,
10_1101-2020_10_26_351783	520	14	and	and	CC
10_1101-2020_10_26_351783	520	15	inflammatory	inflammatory	JJ
10_1101-2020_10_26_351783	520	16	(	(	-LRB-
10_1101-2020_10_26_351783	520	17	green	green	JJ
10_1101-2020_10_26_351783	520	18	)	)	-RRB-
10_1101-2020_10_26_351783	520	19	;	;	:
10_1101-2020_10_26_351783	520	20	and	and	CC
10_1101-2020_10_26_351783	520	21	two	two	CD
10_1101-2020_10_26_351783	520	22	cell	cell	NN
10_1101-2020_10_26_351783	520	23	types	type	NNS
10_1101-2020_10_26_351783	520	24	:	:	:
10_1101-2020_10_26_351783	520	25	blood	blood	NN
10_1101-2020_10_26_351783	520	26	(	(	-LRB-
10_1101-2020_10_26_351783	520	27	maroon	maroon	NNP
10_1101-2020_10_26_351783	520	28	)	)	-RRB-
10_1101-2020_10_26_351783	520	29	,	,	,
10_1101-2020_10_26_351783	520	30	and	and	CC
10_1101-2020_10_26_351783	520	31	others	other	NNS
10_1101-2020_10_26_351783	520	32	(	(	-LRB-
10_1101-2020_10_26_351783	520	33	light	light	JJ
10_1101-2020_10_26_351783	520	34	yellow	yellow	NN
10_1101-2020_10_26_351783	520	35	)	)	-RRB-
10_1101-2020_10_26_351783	520	36	.	.	.
10_1101-2020_10_26_351783	521	1	Datasets	dataset	NNS
10_1101-2020_10_26_351783	521	2	are	be	VBP
10_1101-2020_10_26_351783	521	3	ranked	rank	VBN
10_1101-2020_10_26_351783	521	4	by	by	IN
10_1101-2020_10_26_351783	521	5	Fisher	Fisher	NNP
10_1101-2020_10_26_351783	521	6	’s	’s	POS
10_1101-2020_10_26_351783	521	7	combined	combine	VBN
10_1101-2020_10_26_351783	521	8	P	p	NN
10_1101-2020_10_26_351783	521	9	of	of	IN
10_1101-2020_10_26_351783	521	10	the	the	DT
10_1101-2020_10_26_351783	521	11	single	single	JJ
10_1101-2020_10_26_351783	521	12	-	-	HYPH
10_1101-2020_10_26_351783	521	13	method	method	NN
10_1101-2020_10_26_351783	521	14	module	module	NN
10_1101-2020_10_26_351783	521	15	P	p	NN
10_1101-2020_10_26_351783	521	16	-	-	HYPH
10_1101-2020_10_26_351783	521	17	values	value	NNS
10_1101-2020_10_26_351783	521	18	across	across	IN
10_1101-2020_10_26_351783	521	19	and	and	CC
10_1101-2020_10_26_351783	521	20	within	within	IN
10_1101-2020_10_26_351783	521	21	their	-PRON-	PRP$
10_1101-2020_10_26_351783	521	22	disease	disease	NN
10_1101-2020_10_26_351783	521	23	types	type	NNS
10_1101-2020_10_26_351783	521	24	(	(	-LRB-
10_1101-2020_10_26_351783	521	25	dataset	dataset	NNP
10_1101-2020_10_26_351783	521	26	score	score	NN
10_1101-2020_10_26_351783	521	27	,	,	,
10_1101-2020_10_26_351783	521	28	bottom	bottom	JJ
10_1101-2020_10_26_351783	521	29	boxplot	boxplot	NN
10_1101-2020_10_26_351783	521	30	)	)	-RRB-
10_1101-2020_10_26_351783	521	31	.	.	.
10_1101-2020_10_26_351783	522	1	MODifieR	MODifieR	NNP
10_1101-2020_10_26_351783	522	2	methods	method	NNS
10_1101-2020_10_26_351783	522	3	are	be	VBP
10_1101-2020_10_26_351783	522	4	organized	organize	VBN
10_1101-2020_10_26_351783	522	5	by	by	IN
10_1101-2020_10_26_351783	522	6	algorithm	algorithm	NNP
10_1101-2020_10_26_351783	522	7	type	type	NN
10_1101-2020_10_26_351783	522	8	:	:	:
10_1101-2020_10_26_351783	522	9	seed	seed	NN
10_1101-2020_10_26_351783	522	10	-	-	HYPH
10_1101-2020_10_26_351783	522	11	based	base	VBN
10_1101-2020_10_26_351783	522	12	(	(	-LRB-
10_1101-2020_10_26_351783	522	13	green	green	NNP
10_1101-2020_10_26_351783	522	14	)	)	-RRB-
10_1101-2020_10_26_351783	522	15	,	,	,
10_1101-2020_10_26_351783	522	16	co	co	JJ
10_1101-2020_10_26_351783	522	17	-	-	NN
10_1101-2020_10_26_351783	522	18	expression-	expression-	JJ
10_1101-2020_10_26_351783	522	19	based	base	VBN
10_1101-2020_10_26_351783	522	20	(	(	-LRB-
10_1101-2020_10_26_351783	522	21	yellow	yellow	NNP
10_1101-2020_10_26_351783	522	22	)	)	-RRB-
10_1101-2020_10_26_351783	522	23	,	,	,
10_1101-2020_10_26_351783	522	24	and	and	CC
10_1101-2020_10_26_351783	522	25	clique	clique	NN
10_1101-2020_10_26_351783	522	26	-	-	HYPH
10_1101-2020_10_26_351783	522	27	based	base	VBN
10_1101-2020_10_26_351783	522	28	(	(	-LRB-
10_1101-2020_10_26_351783	522	29	red	red	NNP
10_1101-2020_10_26_351783	522	30	)	)	-RRB-
10_1101-2020_10_26_351783	522	31	,	,	,
10_1101-2020_10_26_351783	522	32	plus	plus	CC
10_1101-2020_10_26_351783	522	33	the	the	DT
10_1101-2020_10_26_351783	522	34	consensus	consensus	NN
10_1101-2020_10_26_351783	522	35	modules	module	NNS
10_1101-2020_10_26_351783	522	36	(	(	-LRB-
10_1101-2020_10_26_351783	522	37	blue	blue	NNP
10_1101-2020_10_26_351783	522	38	)	)	-RRB-
10_1101-2020_10_26_351783	522	39	.	.	.
10_1101-2020_10_26_351783	523	1	Single	single	JJ
10_1101-2020_10_26_351783	523	2	-	-	HYPH
10_1101-2020_10_26_351783	523	3	methods	method	NNS
10_1101-2020_10_26_351783	523	4	and	and	CC
10_1101-2020_10_26_351783	523	5	consensus	consensus	NN
10_1101-2020_10_26_351783	523	6	are	be	VBP
10_1101-2020_10_26_351783	523	7	scored	score	VBN
10_1101-2020_10_26_351783	523	8	by	by	IN
10_1101-2020_10_26_351783	523	9	meta	meta	JJ
10_1101-2020_10_26_351783	523	10	P	p	NN
10_1101-2020_10_26_351783	523	11	-	-	HYPH
10_1101-2020_10_26_351783	523	12	values	value	NNS
10_1101-2020_10_26_351783	523	13	across	across	IN
10_1101-2020_10_26_351783	523	14	datasets	dataset	NNS
10_1101-2020_10_26_351783	523	15	(	(	-LRB-
10_1101-2020_10_26_351783	523	16	method	method	NN
10_1101-2020_10_26_351783	523	17	score	score	NN
10_1101-2020_10_26_351783	523	18	,	,	,
10_1101-2020_10_26_351783	523	19	right	right	JJ
10_1101-2020_10_26_351783	523	20	boxplot	boxplot	NN
10_1101-2020_10_26_351783	523	21	)	)	-RRB-
10_1101-2020_10_26_351783	523	22	.	.	.
10_1101-2020_10_26_351783	524	1	Consensus	Consensus	NNP
10_1101-2020_10_26_351783	524	2	x/8	x/8	NN
10_1101-2020_10_26_351783	524	3	indicates	indicate	VBZ
10_1101-2020_10_26_351783	524	4	that	that	IN
10_1101-2020_10_26_351783	524	5	the	the	DT
10_1101-2020_10_26_351783	524	6	module	module	JJ
10_1101-2020_10_26_351783	524	7	genes	gene	NNS
10_1101-2020_10_26_351783	524	8	are	be	VBP
10_1101-2020_10_26_351783	524	9	found	find	VBN
10_1101-2020_10_26_351783	524	10	in	in	IN
10_1101-2020_10_26_351783	524	11	at	at	IN
10_1101-2020_10_26_351783	524	12	least	least	JJS
10_1101-2020_10_26_351783	524	13	x	x	DT
10_1101-2020_10_26_351783	524	14	methods	method	NNS
10_1101-2020_10_26_351783	524	15	out	out	IN
10_1101-2020_10_26_351783	524	16	of	of	IN
10_1101-2020_10_26_351783	524	17	eight	eight	CD
10_1101-2020_10_26_351783	524	18	.	.	.
10_1101-2020_10_26_351783	525	1	(	(	-LRB-
10_1101-2020_10_26_351783	525	2	b	b	LS
10_1101-2020_10_26_351783	525	3	)	)	-RRB-
10_1101-2020_10_26_351783	525	4	Scatter	scatter	NN
10_1101-2020_10_26_351783	525	5	plot	plot	NN
10_1101-2020_10_26_351783	525	6	(	(	-LRB-
10_1101-2020_10_26_351783	525	7	which	which	WDT
10_1101-2020_10_26_351783	525	8	was	be	VBD
10_1101-2020_10_26_351783	525	9	not	not	RB
10_1101-2020_10_26_351783	525	10	certified	certify	VBN
10_1101-2020_10_26_351783	525	11	by	by	IN
10_1101-2020_10_26_351783	525	12	peer	peer	NN
10_1101-2020_10_26_351783	525	13	review	review	NN
10_1101-2020_10_26_351783	525	14	)	)	-RRB-
10_1101-2020_10_26_351783	525	15	is	be	VBZ
10_1101-2020_10_26_351783	525	16	the	the	DT
10_1101-2020_10_26_351783	525	17	author	author	NN
10_1101-2020_10_26_351783	525	18	/	/	SYM
10_1101-2020_10_26_351783	525	19	funder	funder	NN
10_1101-2020_10_26_351783	525	20	.	.	.
10_1101-2020_10_26_351783	526	1	All	all	DT
10_1101-2020_10_26_351783	526	2	rights	right	NNS
10_1101-2020_10_26_351783	526	3	reserved	reserve	VBD
10_1101-2020_10_26_351783	526	4	.	.	.
10_1101-2020_10_26_351783	527	1	No	no	DT
10_1101-2020_10_26_351783	527	2	reuse	reuse	NN
10_1101-2020_10_26_351783	527	3	allowed	allow	VBN
10_1101-2020_10_26_351783	527	4	without	without	IN
10_1101-2020_10_26_351783	527	5	permission	permission	NN
10_1101-2020_10_26_351783	527	6	.	.	.
10_1101-2020_10_26_351783	528	1	The	the	DT
10_1101-2020_10_26_351783	528	2	copyright	copyright	NN
10_1101-2020_10_26_351783	528	3	holder	holder	NN
10_1101-2020_10_26_351783	528	4	for	for	IN
10_1101-2020_10_26_351783	528	5	this	this	DT
10_1101-2020_10_26_351783	528	6	preprintthis	preprintthis	NN
10_1101-2020_10_26_351783	528	7	version	version	NN
10_1101-2020_10_26_351783	528	8	posted	post	VBD
10_1101-2020_10_26_351783	528	9	January	January	NNP
10_1101-2020_10_26_351783	528	10	6	6	CD
10_1101-2020_10_26_351783	528	11	,	,	,
10_1101-2020_10_26_351783	528	12	2021	2021	CD
10_1101-2020_10_26_351783	528	13	.	.	.
10_1101-2020_10_26_351783	528	14	;	;	:
10_1101-2020_10_26_351783	528	15	https://doi.org/10.1101/2020.10.26.351783doi	https://doi.org/10.1101/2020.10.26.351783doi	ADD
10_1101-2020_10_26_351783	528	16	:	:	:
10_1101-2020_10_26_351783	528	17	bioRxiv	biorxiv	IN
10_1101-2020_10_26_351783	528	18	preprint	preprint	NN
10_1101-2020_10_26_351783	528	19	https://doi.org/10.1101/2020.10.26.351783	https://doi.org/10.1101/2020.10.26.351783	CC
10_1101-2020_10_26_351783	528	20	30	30	CD
10_1101-2020_10_26_351783	528	21	showing	show	VBG
10_1101-2020_10_26_351783	528	22	Spearman	Spearman	NNP
10_1101-2020_10_26_351783	528	23	correlation	correlation	NN
10_1101-2020_10_26_351783	528	24	between	between	IN
10_1101-2020_10_26_351783	528	25	module	module	JJ
10_1101-2020_10_26_351783	528	26	score	score	NN
10_1101-2020_10_26_351783	528	27	and	and	CC
10_1101-2020_10_26_351783	528	28	betweenness	betweenness	JJ
10_1101-2020_10_26_351783	528	29	centrality	centrality	NN
10_1101-2020_10_26_351783	528	30	.	.	.
10_1101-2020_10_26_351783	529	1	Modules	module	NNS
10_1101-2020_10_26_351783	529	2	are	be	VBP
10_1101-2020_10_26_351783	529	3	represented	represent	VBN
10_1101-2020_10_26_351783	529	4	with	with	IN
10_1101-2020_10_26_351783	529	5	a	a	DT
10_1101-2020_10_26_351783	529	6	different	different	JJ
10_1101-2020_10_26_351783	529	7	shape	shape	NN
10_1101-2020_10_26_351783	529	8	depending	depend	VBG
10_1101-2020_10_26_351783	529	9	on	on	IN
10_1101-2020_10_26_351783	529	10	their	-PRON-	PRP$
10_1101-2020_10_26_351783	529	11	method	method	NN
10_1101-2020_10_26_351783	529	12	and	and	CC
10_1101-2020_10_26_351783	529	13	colored	color	VBN
10_1101-2020_10_26_351783	529	14	based	base	VBN
10_1101-2020_10_26_351783	529	15	on	on	IN
10_1101-2020_10_26_351783	529	16	the	the	DT
10_1101-2020_10_26_351783	529	17	disease	disease	NN
10_1101-2020_10_26_351783	529	18	type	type	NN
10_1101-2020_10_26_351783	529	19	.	.	.
10_1101-2020_10_26_351783	530	1	(	(	-LRB-
10_1101-2020_10_26_351783	530	2	c	c	NN
10_1101-2020_10_26_351783	530	3	)	)	-RRB-
10_1101-2020_10_26_351783	530	4	Scatter	scatter	NN
10_1101-2020_10_26_351783	530	5	plot	plot	NN
10_1101-2020_10_26_351783	530	6	showing	show	VBG
10_1101-2020_10_26_351783	530	7	Spearman	spearman	NN
10_1101-2020_10_26_351783	530	8	correlation	correlation	NN
10_1101-2020_10_26_351783	530	9	between	between	IN
10_1101-2020_10_26_351783	530	10	module	module	JJ
10_1101-2020_10_26_351783	530	11	score	score	NN
10_1101-2020_10_26_351783	530	12	and	and	CC
10_1101-2020_10_26_351783	530	13	module	module	JJ
10_1101-2020_10_26_351783	530	14	size	size	NN
10_1101-2020_10_26_351783	530	15	.	.	.
10_1101-2020_10_26_351783	531	1	Modules	module	NNS
10_1101-2020_10_26_351783	531	2	are	be	VBP
10_1101-2020_10_26_351783	531	3	represented	represent	VBN
10_1101-2020_10_26_351783	531	4	with	with	IN
10_1101-2020_10_26_351783	531	5	a	a	DT
10_1101-2020_10_26_351783	531	6	different	different	JJ
10_1101-2020_10_26_351783	531	7	shape	shape	NN
10_1101-2020_10_26_351783	531	8	depending	depend	VBG
10_1101-2020_10_26_351783	531	9	on	on	IN
10_1101-2020_10_26_351783	531	10	their	-PRON-	PRP$
10_1101-2020_10_26_351783	531	11	method	method	NN
10_1101-2020_10_26_351783	531	12	and	and	CC
10_1101-2020_10_26_351783	531	13	colored	color	VBN
10_1101-2020_10_26_351783	531	14	based	base	VBN
10_1101-2020_10_26_351783	531	15	on	on	IN
10_1101-2020_10_26_351783	531	16	the	the	DT
10_1101-2020_10_26_351783	531	17	disease	disease	NN
10_1101-2020_10_26_351783	531	18	type	type	NN
10_1101-2020_10_26_351783	531	19	.	.	.
10_1101-2020_10_26_351783	532	1	(	(	-LRB-
10_1101-2020_10_26_351783	532	2	which	which	WDT
10_1101-2020_10_26_351783	532	3	was	be	VBD
10_1101-2020_10_26_351783	532	4	not	not	RB
10_1101-2020_10_26_351783	532	5	certified	certify	VBN
10_1101-2020_10_26_351783	532	6	by	by	IN
10_1101-2020_10_26_351783	532	7	peer	peer	NN
10_1101-2020_10_26_351783	532	8	review	review	NN
10_1101-2020_10_26_351783	532	9	)	)	-RRB-
10_1101-2020_10_26_351783	532	10	is	be	VBZ
10_1101-2020_10_26_351783	532	11	the	the	DT
10_1101-2020_10_26_351783	532	12	author	author	NN
10_1101-2020_10_26_351783	532	13	/	/	SYM
10_1101-2020_10_26_351783	532	14	funder	funder	NN
10_1101-2020_10_26_351783	532	15	.	.	.
10_1101-2020_10_26_351783	533	1	All	all	DT
10_1101-2020_10_26_351783	533	2	rights	right	NNS
10_1101-2020_10_26_351783	533	3	reserved	reserve	VBD
10_1101-2020_10_26_351783	533	4	.	.	.
10_1101-2020_10_26_351783	534	1	No	no	DT
10_1101-2020_10_26_351783	534	2	reuse	reuse	NN
10_1101-2020_10_26_351783	534	3	allowed	allow	VBN
10_1101-2020_10_26_351783	534	4	without	without	IN
10_1101-2020_10_26_351783	534	5	permission	permission	NN
10_1101-2020_10_26_351783	534	6	.	.	.
10_1101-2020_10_26_351783	535	1	The	the	DT
10_1101-2020_10_26_351783	535	2	copyright	copyright	NN
10_1101-2020_10_26_351783	535	3	holder	holder	NN
10_1101-2020_10_26_351783	535	4	for	for	IN
10_1101-2020_10_26_351783	535	5	this	this	DT
10_1101-2020_10_26_351783	535	6	preprintthis	preprintthis	NN
10_1101-2020_10_26_351783	535	7	version	version	NN
10_1101-2020_10_26_351783	535	8	posted	post	VBD
10_1101-2020_10_26_351783	535	9	January	January	NNP
10_1101-2020_10_26_351783	535	10	6	6	CD
10_1101-2020_10_26_351783	535	11	,	,	,
10_1101-2020_10_26_351783	535	12	2021	2021	CD
10_1101-2020_10_26_351783	535	13	.	.	.
10_1101-2020_10_26_351783	535	14	;	;	:
10_1101-2020_10_26_351783	535	15	https://doi.org/10.1101/2020.10.26.351783doi	https://doi.org/10.1101/2020.10.26.351783doi	ADD
10_1101-2020_10_26_351783	535	16	:	:	:
10_1101-2020_10_26_351783	535	17	bioRxiv	biorxiv	IN
10_1101-2020_10_26_351783	535	18	preprint	preprint	NN
10_1101-2020_10_26_351783	535	19	https://doi.org/10.1101/2020.10.26.351783	https://doi.org/10.1101/2020.10.26.351783	CC
10_1101-2020_10_26_351783	535	20	31	31	CD
10_1101-2020_10_26_351783	535	21	Figure	figure	NN
10_1101-2020_10_26_351783	535	22	4	4	CD
10_1101-2020_10_26_351783	535	23	.	.	.
10_1101-2020_10_26_351783	536	1	Genomic	genomic	JJ
10_1101-2020_10_26_351783	536	2	concordance	concordance	NN
10_1101-2020_10_26_351783	536	3	of	of	IN
10_1101-2020_10_26_351783	536	4	MODifieR	MODifieR	NNP
10_1101-2020_10_26_351783	536	5	modules	module	NNS
10_1101-2020_10_26_351783	536	6	on	on	IN
10_1101-2020_10_26_351783	536	7	MS	MS	NNP
10_1101-2020_10_26_351783	536	8	use	use	VBP
10_1101-2020_10_26_351783	536	9	case	case	NN
10_1101-2020_10_26_351783	536	10	data	datum	NNS
10_1101-2020_10_26_351783	536	11	.	.	.
10_1101-2020_10_26_351783	537	1	(	(	-LRB-
10_1101-2020_10_26_351783	537	2	a	a	DT
10_1101-2020_10_26_351783	537	3	)	)	-RRB-
10_1101-2020_10_26_351783	537	4	Heatmap	Heatmap	NNP
10_1101-2020_10_26_351783	537	5	of	of	IN
10_1101-2020_10_26_351783	537	6	PASCAL	PASCAL	NNP
10_1101-2020_10_26_351783	537	7	p	p	NN
10_1101-2020_10_26_351783	537	8	-	-	HYPH
10_1101-2020_10_26_351783	537	9	values	value	NNS
10_1101-2020_10_26_351783	537	10	for	for	IN
10_1101-2020_10_26_351783	537	11	eight	eight	CD
10_1101-2020_10_26_351783	537	12	single	single	JJ
10_1101-2020_10_26_351783	537	13	-	-	HYPH
10_1101-2020_10_26_351783	537	14	method	method	NN
10_1101-2020_10_26_351783	537	15	MODifieR	MODifieR	NNP
10_1101-2020_10_26_351783	537	16	modules	module	NNS
10_1101-2020_10_26_351783	537	17	,	,	,
10_1101-2020_10_26_351783	537	18	identified	identify	VBN
10_1101-2020_10_26_351783	537	19	for	for	IN
10_1101-2020_10_26_351783	537	20	ten	ten	CD
10_1101-2020_10_26_351783	537	21	MS	MS	NNP
10_1101-2020_10_26_351783	537	22	-	-	HYPH
10_1101-2020_10_26_351783	537	23	related	relate	VBN
10_1101-2020_10_26_351783	537	24	transcriptomic	transcriptomic	JJ
10_1101-2020_10_26_351783	537	25	datasets	dataset	NNS
10_1101-2020_10_26_351783	537	26	.	.	.
10_1101-2020_10_26_351783	538	1	Module	module	JJ
10_1101-2020_10_26_351783	538	2	performance	performance	NN
10_1101-2020_10_26_351783	538	3	P	p	NN
10_1101-2020_10_26_351783	538	4	-	-	HYPH
10_1101-2020_10_26_351783	538	5	values	value	NNS
10_1101-2020_10_26_351783	538	6	are	be	VBP
10_1101-2020_10_26_351783	538	7	shown	show	VBN
10_1101-2020_10_26_351783	538	8	in	in	IN
10_1101-2020_10_26_351783	538	9	a	a	DT
10_1101-2020_10_26_351783	538	10	white	white	NN
10_1101-2020_10_26_351783	538	11	to	to	IN
10_1101-2020_10_26_351783	538	12	blue	blue	JJ
10_1101-2020_10_26_351783	538	13	scale	scale	NN
10_1101-2020_10_26_351783	538	14	,	,	,
10_1101-2020_10_26_351783	538	15	where	where	WRB
10_1101-2020_10_26_351783	538	16	any	any	DT
10_1101-2020_10_26_351783	538	17	shade	shade	NN
10_1101-2020_10_26_351783	538	18	of	of	IN
10_1101-2020_10_26_351783	538	19	blue	blue	JJ
10_1101-2020_10_26_351783	538	20	represents	represent	VBZ
10_1101-2020_10_26_351783	538	21	a	a	DT
10_1101-2020_10_26_351783	538	22	significant	significant	JJ
10_1101-2020_10_26_351783	538	23	module	module	NN
10_1101-2020_10_26_351783	538	24	(	(	-LRB-
10_1101-2020_10_26_351783	538	25	P	p	NN
10_1101-2020_10_26_351783	538	26	<	<	XX
10_1101-2020_10_26_351783	538	27	0.05	0.05	XX
10_1101-2020_10_26_351783	538	28	)	)	-RRB-
10_1101-2020_10_26_351783	538	29	,	,	,
10_1101-2020_10_26_351783	538	30	white	white	NNP
10_1101-2020_10_26_351783	538	31	represents	represent	VBZ
10_1101-2020_10_26_351783	538	32	a	a	DT
10_1101-2020_10_26_351783	538	33	non	non	JJ
10_1101-2020_10_26_351783	538	34	-	-	JJ
10_1101-2020_10_26_351783	538	35	significant	significant	JJ
10_1101-2020_10_26_351783	538	36	module	module	NN
10_1101-2020_10_26_351783	538	37	,	,	,
10_1101-2020_10_26_351783	538	38	and	and	CC
10_1101-2020_10_26_351783	538	39	grey	grey	NNP
10_1101-2020_10_26_351783	538	40	represents	represent	VBZ
10_1101-2020_10_26_351783	538	41	a	a	DT
10_1101-2020_10_26_351783	538	42	module	module	NN
10_1101-2020_10_26_351783	538	43	of	of	IN
10_1101-2020_10_26_351783	538	44	size	size	NN
10_1101-2020_10_26_351783	538	45	zero	zero	CD
10_1101-2020_10_26_351783	538	46	.	.	.
10_1101-2020_10_26_351783	539	1	Datasets	dataset	NNS
10_1101-2020_10_26_351783	539	2	are	be	VBP
10_1101-2020_10_26_351783	539	3	classified	classify	VBN
10_1101-2020_10_26_351783	539	4	into	into	IN
10_1101-2020_10_26_351783	539	5	the	the	DT
10_1101-2020_10_26_351783	539	6	reported	report	VBN
10_1101-2020_10_26_351783	539	7	MS	MS	NNP
10_1101-2020_10_26_351783	539	8	type	type	NN
10_1101-2020_10_26_351783	539	9	:	:	:
10_1101-2020_10_26_351783	539	10	MS	MS	NNP
10_1101-2020_10_26_351783	539	11	(	(	-LRB-
10_1101-2020_10_26_351783	539	12	blue	blue	NNP
10_1101-2020_10_26_351783	539	13	)	)	-RRB-
10_1101-2020_10_26_351783	539	14	,	,	,
10_1101-2020_10_26_351783	539	15	RRMS	RRMS	NNP
10_1101-2020_10_26_351783	539	16	(	(	-LRB-
10_1101-2020_10_26_351783	539	17	red	red	NNP
10_1101-2020_10_26_351783	539	18	)	)	-RRB-
10_1101-2020_10_26_351783	539	19	,	,	,
10_1101-2020_10_26_351783	539	20	PPMS	PPMS	NNP
10_1101-2020_10_26_351783	539	21	(	(	-LRB-
10_1101-2020_10_26_351783	539	22	green	green	NNP
10_1101-2020_10_26_351783	539	23	)	)	-RRB-
10_1101-2020_10_26_351783	539	24	,	,	,
10_1101-2020_10_26_351783	539	25	SPMS	SPMS	NNP
10_1101-2020_10_26_351783	539	26	(	(	-LRB-
10_1101-2020_10_26_351783	539	27	orange	orange	NNP
10_1101-2020_10_26_351783	539	28	)	)	-RRB-
10_1101-2020_10_26_351783	539	29	,	,	,
10_1101-2020_10_26_351783	539	30	and	and	CC
10_1101-2020_10_26_351783	539	31	CIS	CIS	NNP
10_1101-2020_10_26_351783	539	32	(	(	-LRB-
10_1101-2020_10_26_351783	539	33	yellow	yellow	NNP
10_1101-2020_10_26_351783	539	34	)	)	-RRB-
10_1101-2020_10_26_351783	539	35	;	;	:
10_1101-2020_10_26_351783	539	36	and	and	CC
10_1101-2020_10_26_351783	539	37	four	four	CD
10_1101-2020_10_26_351783	539	38	cell	cell	NN
10_1101-2020_10_26_351783	539	39	types	type	NNS
10_1101-2020_10_26_351783	539	40	:	:	:
10_1101-2020_10_26_351783	539	41	whole	whole	JJ
10_1101-2020_10_26_351783	539	42	blood	blood	NN
10_1101-2020_10_26_351783	539	43	(	(	-LRB-
10_1101-2020_10_26_351783	539	44	maroon	maroon	NNP
10_1101-2020_10_26_351783	539	45	)	)	-RRB-
10_1101-2020_10_26_351783	539	46	,	,	,
10_1101-2020_10_26_351783	539	47	PBMCs	PBMCs	NNP
10_1101-2020_10_26_351783	539	48	(	(	-LRB-
10_1101-2020_10_26_351783	539	49	light	light	JJ
10_1101-2020_10_26_351783	539	50	brown	brown	NNP
10_1101-2020_10_26_351783	539	51	)	)	-RRB-
10_1101-2020_10_26_351783	539	52	,	,	,
10_1101-2020_10_26_351783	539	53	white	white	NNP
10_1101-2020_10_26_351783	539	54	matter	matter	NNP
10_1101-2020_10_26_351783	539	55	(	(	-LRB-
10_1101-2020_10_26_351783	539	56	light	light	JJ
10_1101-2020_10_26_351783	539	57	yellow	yellow	NNP
10_1101-2020_10_26_351783	539	58	)	)	-RRB-
10_1101-2020_10_26_351783	539	59	,	,	,
10_1101-2020_10_26_351783	539	60	and	and	CC
10_1101-2020_10_26_351783	539	61	CD4	cd4	NN
10_1101-2020_10_26_351783	539	62	+	+	SYM
10_1101-2020_10_26_351783	539	63	T	t	NN
10_1101-2020_10_26_351783	539	64	cells	cell	NNS
10_1101-2020_10_26_351783	539	65	(	(	-LRB-
10_1101-2020_10_26_351783	539	66	purple	purple	NNP
10_1101-2020_10_26_351783	539	67	)	)	-RRB-
10_1101-2020_10_26_351783	539	68	.	.	.
10_1101-2020_10_26_351783	540	1	Datasets	dataset	NNS
10_1101-2020_10_26_351783	540	2	are	be	VBP
10_1101-2020_10_26_351783	540	3	meta	meta	JJ
10_1101-2020_10_26_351783	540	4	P-	p-	JJ
10_1101-2020_10_26_351783	540	5	values	value	NNS
10_1101-2020_10_26_351783	540	6	of	of	IN
10_1101-2020_10_26_351783	540	7	the	the	DT
10_1101-2020_10_26_351783	540	8	single	single	JJ
10_1101-2020_10_26_351783	540	9	-	-	HYPH
10_1101-2020_10_26_351783	540	10	method	method	NN
10_1101-2020_10_26_351783	540	11	enrichments	enrichment	NNS
10_1101-2020_10_26_351783	540	12	(	(	-LRB-
10_1101-2020_10_26_351783	540	13	dataset	dataset	NNP
10_1101-2020_10_26_351783	540	14	score	score	NN
10_1101-2020_10_26_351783	540	15	,	,	,
10_1101-2020_10_26_351783	540	16	bottom	bottom	JJ
10_1101-2020_10_26_351783	540	17	boxplot	boxplot	NN
10_1101-2020_10_26_351783	540	18	)	)	-RRB-
10_1101-2020_10_26_351783	540	19	.	.	.
10_1101-2020_10_26_351783	541	1	MODifieR	MODifieR	NNP
10_1101-2020_10_26_351783	541	2	methods	method	NNS
10_1101-2020_10_26_351783	541	3	are	be	VBP
10_1101-2020_10_26_351783	541	4	organized	organize	VBN
10_1101-2020_10_26_351783	541	5	by	by	IN
10_1101-2020_10_26_351783	541	6	algorithm	algorithm	NNP
10_1101-2020_10_26_351783	541	7	type	type	NN
10_1101-2020_10_26_351783	541	8	:	:	:
10_1101-2020_10_26_351783	541	9	seed	seed	NN
10_1101-2020_10_26_351783	541	10	-	-	HYPH
10_1101-2020_10_26_351783	541	11	based	base	VBN
10_1101-2020_10_26_351783	541	12	(	(	-LRB-
10_1101-2020_10_26_351783	541	13	green	green	NNP
10_1101-2020_10_26_351783	541	14	)	)	-RRB-
10_1101-2020_10_26_351783	541	15	,	,	,
10_1101-2020_10_26_351783	541	16	co	co	NN
10_1101-2020_10_26_351783	541	17	-	-	NN
10_1101-2020_10_26_351783	541	18	expression	expression	NN
10_1101-2020_10_26_351783	541	19	-	-	HYPH
10_1101-2020_10_26_351783	541	20	based	base	VBN
10_1101-2020_10_26_351783	541	21	(	(	-LRB-
10_1101-2020_10_26_351783	541	22	yellow	yellow	NNP
10_1101-2020_10_26_351783	541	23	)	)	-RRB-
10_1101-2020_10_26_351783	541	24	,	,	,
10_1101-2020_10_26_351783	541	25	and	and	CC
10_1101-2020_10_26_351783	541	26	clique	clique	NN
10_1101-2020_10_26_351783	541	27	-	-	HYPH
10_1101-2020_10_26_351783	541	28	based	base	VBN
10_1101-2020_10_26_351783	541	29	(	(	-LRB-
10_1101-2020_10_26_351783	541	30	red	red	NNP
10_1101-2020_10_26_351783	541	31	)	)	-RRB-
10_1101-2020_10_26_351783	541	32	.	.	.
10_1101-2020_10_26_351783	542	1	Single	single	JJ
10_1101-2020_10_26_351783	542	2	methods	method	NNS
10_1101-2020_10_26_351783	542	3	are	be	VBP
10_1101-2020_10_26_351783	542	4	scored	score	VBN
10_1101-2020_10_26_351783	542	5	by	by	IN
10_1101-2020_10_26_351783	542	6	P	p	NN
10_1101-2020_10_26_351783	542	7	of	of	IN
10_1101-2020_10_26_351783	542	8	the	the	DT
10_1101-2020_10_26_351783	542	9	significant	significant	JJ
10_1101-2020_10_26_351783	542	10	modules	module	NNS
10_1101-2020_10_26_351783	542	11	across	across	IN
10_1101-2020_10_26_351783	542	12	datasets	dataset	NNS
10_1101-2020_10_26_351783	542	13	(	(	-LRB-
10_1101-2020_10_26_351783	542	14	method	method	NN
10_1101-2020_10_26_351783	542	15	score	score	NN
10_1101-2020_10_26_351783	542	16	,	,	,
10_1101-2020_10_26_351783	542	17	right	right	JJ
10_1101-2020_10_26_351783	542	18	boxplot	boxplot	NN
10_1101-2020_10_26_351783	542	19	)	)	-RRB-
10_1101-2020_10_26_351783	542	20	.	.	.
10_1101-2020_10_26_351783	543	1	(	(	-LRB-
10_1101-2020_10_26_351783	543	2	b	b	NN
10_1101-2020_10_26_351783	543	3	)	)	-RRB-
10_1101-2020_10_26_351783	543	4	Heatmap	Heatmap	NNP
10_1101-2020_10_26_351783	543	5	of	of	IN
10_1101-2020_10_26_351783	543	6	PASCAL	PASCAL	NNP
10_1101-2020_10_26_351783	543	7	p	p	NN
10_1101-2020_10_26_351783	543	8	-	-	HYPH
10_1101-2020_10_26_351783	543	9	values	value	NNS
10_1101-2020_10_26_351783	543	10	for	for	IN
10_1101-2020_10_26_351783	543	11	four	four	CD
10_1101-2020_10_26_351783	543	12	single	single	JJ
10_1101-2020_10_26_351783	543	13	-	-	HYPH
10_1101-2020_10_26_351783	543	14	method	method	NN
10_1101-2020_10_26_351783	543	15	MODifieR	MODifieR	NNP
10_1101-2020_10_26_351783	543	16	modules	module	NNS
10_1101-2020_10_26_351783	543	17	,	,	,
10_1101-2020_10_26_351783	543	18	identified	identify	VBN
10_1101-2020_10_26_351783	543	19	for	for	IN
10_1101-2020_10_26_351783	543	20	nine	nine	CD
10_1101-2020_10_26_351783	543	21	MS	MS	NNP
10_1101-2020_10_26_351783	543	22	-	-	HYPH
10_1101-2020_10_26_351783	543	23	related	relate	VBN
10_1101-2020_10_26_351783	543	24	transcriptomic	transcriptomic	JJ
10_1101-2020_10_26_351783	543	25	datasets	dataset	NNS
10_1101-2020_10_26_351783	543	26	.	.	.
10_1101-2020_10_26_351783	544	1	(	(	-LRB-
10_1101-2020_10_26_351783	544	2	c	c	NN
10_1101-2020_10_26_351783	544	3	-	-	HYPH
10_1101-2020_10_26_351783	544	4	d	d	NNP
10_1101-2020_10_26_351783	544	5	)	)	-RRB-
10_1101-2020_10_26_351783	544	6	Bar	bar	NN
10_1101-2020_10_26_351783	544	7	plots	plot	NNS
10_1101-2020_10_26_351783	544	8	of	of	IN
10_1101-2020_10_26_351783	544	9	Pascal	pascal	JJ
10_1101-2020_10_26_351783	544	10	p	p	NN
10_1101-2020_10_26_351783	544	11	-	-	HYPH
10_1101-2020_10_26_351783	544	12	values	value	NNS
10_1101-2020_10_26_351783	544	13	for	for	IN
10_1101-2020_10_26_351783	544	14	the	the	DT
10_1101-2020_10_26_351783	544	15	MS	MS	NNP
10_1101-2020_10_26_351783	544	16	consensus	consensus	NN
10_1101-2020_10_26_351783	544	17	modules	module	NNS
10_1101-2020_10_26_351783	544	18	generated	generate	VBN
10_1101-2020_10_26_351783	544	19	with	with	IN
10_1101-2020_10_26_351783	544	20	Clique	Clique	NNP
10_1101-2020_10_26_351783	544	21	SuM	sum	NN
10_1101-2020_10_26_351783	544	22	from	from	IN
10_1101-2020_10_26_351783	544	23	transcriptomic	transcriptomic	NNP
10_1101-2020_10_26_351783	544	24	(	(	-LRB-
10_1101-2020_10_26_351783	544	25	a	a	DT
10_1101-2020_10_26_351783	544	26	)	)	-RRB-
10_1101-2020_10_26_351783	544	27	and	and	CC
10_1101-2020_10_26_351783	544	28	methylomic	methylomic	NNP
10_1101-2020_10_26_351783	544	29	(	(	-LRB-
10_1101-2020_10_26_351783	544	30	b	b	NN
10_1101-2020_10_26_351783	544	31	)	)	-RRB-
10_1101-2020_10_26_351783	544	32	datasets	dataset	NNS
10_1101-2020_10_26_351783	544	33	.	.	.
10_1101-2020_10_26_351783	545	1	(	(	-LRB-
10_1101-2020_10_26_351783	545	2	e	e	LS
10_1101-2020_10_26_351783	545	3	)	)	-RRB-
10_1101-2020_10_26_351783	545	4	Union	union	NN
10_1101-2020_10_26_351783	545	5	and	and	CC
10_1101-2020_10_26_351783	545	6	intersection	intersection	NN
10_1101-2020_10_26_351783	545	7	of	of	IN
10_1101-2020_10_26_351783	545	8	the	the	DT
10_1101-2020_10_26_351783	545	9	top	top	JJ
10_1101-2020_10_26_351783	545	10	performing	perform	VBG
10_1101-2020_10_26_351783	545	11	modules	module	NNS
10_1101-2020_10_26_351783	545	12	,	,	,
10_1101-2020_10_26_351783	545	13	shown	show	VBN
10_1101-2020_10_26_351783	545	14	as	as	IN
10_1101-2020_10_26_351783	545	15	a	a	DT
10_1101-2020_10_26_351783	545	16	Venn	Venn	NNP
10_1101-2020_10_26_351783	545	17	diagram	diagram	NN
10_1101-2020_10_26_351783	545	18	.	.	.
10_1101-2020_10_26_351783	546	1	Diseas	Diseas	NNP
10_1101-2020_10_26_351783	546	2	e	e	NNP
10_1101-2020_10_26_351783	546	3	Type	Type	NNP
10_1101-2020_10_26_351783	546	4	MS	MS	NNP
10_1101-2020_10_26_351783	546	5	RRMS	RRMS	NNP
10_1101-2020_10_26_351783	546	6	PPMS	PPMS	NNP
10_1101-2020_10_26_351783	546	7	SPMS	SPMS	NNP
10_1101-2020_10_26_351783	546	8	CIS	CIS	NNP
10_1101-2020_10_26_351783	546	9	Module	Module	NNP
10_1101-2020_10_26_351783	546	10	Performance	Performance	NNP
10_1101-2020_10_26_351783	546	11	α	α	NNP
10_1101-2020_10_26_351783	546	12	=	=	NNS
10_1101-2020_10_26_351783	546	13	.05	.05	CD
10_1101-2020_10_26_351783	546	14	1	1	CD
10_1101-2020_10_26_351783	546	15	10	10	CD
10_1101-2020_10_26_351783	546	16	-	-	SYM
10_1101-2020_10_26_351783	546	17	2	2	CD
10_1101-2020_10_26_351783	546	18	10	10	CD
10_1101-2020_10_26_351783	546	19	-	-	SYM
10_1101-2020_10_26_351783	546	20	3	3	CD
10_1101-2020_10_26_351783	546	21	10	10	CD
10_1101-2020_10_26_351783	546	22	-	-	SYM
10_1101-2020_10_26_351783	546	23	4	4	CD
10_1101-2020_10_26_351783	546	24	≤10	≤10	NNP
10_1101-2020_10_26_351783	546	25	-	-	HYPH
10_1101-2020_10_26_351783	546	26	5Best	5best	NN
10_1101-2020_10_26_351783	546	27	Worst	bad	JJS
10_1101-2020_10_26_351783	546	28	P	p	NN
10_1101-2020_10_26_351783	546	29	Cell	Cell	NNP
10_1101-2020_10_26_351783	546	30	Type	Type	NNP
10_1101-2020_10_26_351783	546	31	WB	WB	NNP
10_1101-2020_10_26_351783	546	32	PBMCs	PBMCs	NNP
10_1101-2020_10_26_351783	546	33	WM	WM	NNP
10_1101-2020_10_26_351783	546	34	CD4	cd4	NN
10_1101-2020_10_26_351783	546	35	+	+	SYM
10_1101-2020_10_26_351783	546	36	T	t	NN
10_1101-2020_10_26_351783	546	37	cells	cell	NNS
10_1101-2020_10_26_351783	546	38	CD14	CD14	NNP
10_1101-2020_10_26_351783	546	39	+	+	CC
10_1101-2020_10_26_351783	546	40	Monocytes	Monocytes	NNP
10_1101-2020_10_26_351783	546	41	CD19	CD19	NNP
10_1101-2020_10_26_351783	546	42	+	+	CC
10_1101-2020_10_26_351783	546	43	B	b	NN
10_1101-2020_10_26_351783	546	44	cells	cell	NNS
10_1101-2020_10_26_351783	546	45	CD8	CD8	NNP
10_1101-2020_10_26_351783	546	46	+	+	CC
10_1101-2020_10_26_351783	546	47	T	t	NN
10_1101-2020_10_26_351783	546	48	cells	cell	NNS
10_1101-2020_10_26_351783	546	49	a	a	DT
10_1101-2020_10_26_351783	546	50	b	b	NN
10_1101-2020_10_26_351783	546	51	c	c	NN
10_1101-2020_10_26_351783	546	52	d	d	NNP
10_1101-2020_10_26_351783	546	53	e	e	NNP
10_1101-2020_10_26_351783	546	54	0	0	CD
10_1101-2020_10_26_351783	546	55	2	2	CD
10_1101-2020_10_26_351783	546	56	4	4	CD
10_1101-2020_10_26_351783	546	57	6	6	CD
10_1101-2020_10_26_351783	546	58	8	8	CD
10_1101-2020_10_26_351783	546	59	1/4	1/4	CD
10_1101-2020_10_26_351783	546	60	2/4	2/4	CD
10_1101-2020_10_26_351783	546	61	3/4	3/4	CD
10_1101-2020_10_26_351783	546	62	4/4	4/4	CD
10_1101-2020_10_26_351783	546	63	Transcriptomic	Transcriptomic	NNP
10_1101-2020_10_26_351783	546	64	Cliq	Cliq	NNP
10_1101-2020_10_26_351783	546	65	ue	ue	JJ
10_1101-2020_10_26_351783	546	66	SuM	sum	NN
10_1101-2020_10_26_351783	546	67	consensus	consensus	NN
10_1101-2020_10_26_351783	546	68	modules	modules	NNP
10_1101-2020_10_26_351783	546	69	α	α	NNP
10_1101-2020_10_26_351783	546	70	-l	-l	NN
10_1101-2020_10_26_351783	546	71	o	o	NN
10_1101-2020_10_26_351783	546	72	g	g	NN
10_1101-2020_10_26_351783	546	73	1	1	CD
10_1101-2020_10_26_351783	546	74	0	0	CD
10_1101-2020_10_26_351783	546	75	P	p	NN
10_1101-2020_10_26_351783	546	76	*	*	NFP
10_1101-2020_10_26_351783	546	77	0	0	CD
10_1101-2020_10_26_351783	546	78	2	2	CD
10_1101-2020_10_26_351783	546	79	4	4	CD
10_1101-2020_10_26_351783	546	80	6	6	CD
10_1101-2020_10_26_351783	546	81	8	8	CD
10_1101-2020_10_26_351783	546	82	α	α	NN
10_1101-2020_10_26_351783	546	83	-l	-l	NN
10_1101-2020_10_26_351783	546	84	o	o	NN
10_1101-2020_10_26_351783	546	85	g	g	NN
10_1101-2020_10_26_351783	546	86	1	1	CD
10_1101-2020_10_26_351783	546	87	0	0	CD
10_1101-2020_10_26_351783	546	88	P	p	NN
10_1101-2020_10_26_351783	546	89	1/4	1/4	CD
10_1101-2020_10_26_351783	546	90	2/4	2/4	CD
10_1101-2020_10_26_351783	546	91	3/4	3/4	CD
10_1101-2020_10_26_351783	546	92	4/4	4/4	CD
10_1101-2020_10_26_351783	546	93	Methylomic	Methylomic	NNP
10_1101-2020_10_26_351783	546	94	Cliq	Cliq	NNP
10_1101-2020_10_26_351783	546	95	ue	ue	NNP
10_1101-2020_10_26_351783	546	96	SuM	SuM	NNP
10_1101-2020_10_26_351783	546	97	consensus	consensus	NN
10_1101-2020_10_26_351783	546	98	modules	module	VBZ
10_1101-2020_10_26_351783	546	99	*	*	NFP
10_1101-2020_10_26_351783	546	100	Best	good	JJS
10_1101-2020_10_26_351783	546	101	transcriptomic	transcriptomic	JJ
10_1101-2020_10_26_351783	546	102	consensus	consensus	NN
10_1101-2020_10_26_351783	546	103	Best	well	RBS
10_1101-2020_10_26_351783	546	104	methylomic	methylomic	JJ
10_1101-2020_10_26_351783	546	105	consensus	consensus	NN
10_1101-2020_10_26_351783	546	106	IntersectionUnion	IntersectionUnion	NNP
10_1101-2020_10_26_351783	546	107	ngenes	ngene	NNS
10_1101-2020_10_26_351783	546	108	1041332	1041332	CD
10_1101-2020_10_26_351783	546	109	220	220	CD
10_1101-2020_10_26_351783	546	110	1656	1656	CD
10_1101-2020_10_26_351783	546	111	*	*	NFP
10_1101-2020_10_26_351783	546	112	(	(	-LRB-
10_1101-2020_10_26_351783	546	113	P	p	NN
10_1101-2020_10_26_351783	546	114	=	=	SYM
10_1101-2020_10_26_351783	546	115	4.82	4.82	CD
10_1101-2020_10_26_351783	546	116	x	x	SYM
10_1101-2020_10_26_351783	546	117	10	10	CD
10_1101-2020_10_26_351783	546	118	-8	-8	.
10_1101-2020_10_26_351783	546	119	)	)	-RRB-
10_1101-2020_10_26_351783	546	120	(	(	-LRB-
10_1101-2020_10_26_351783	546	121	P	p	NN
10_1101-2020_10_26_351783	546	122	=	=	SYM
10_1101-2020_10_26_351783	546	123	3.74	3.74	CD
10_1101-2020_10_26_351783	546	124	x	x	SYM
10_1101-2020_10_26_351783	546	125	10	10	CD
10_1101-2020_10_26_351783	546	126	-8	-8	.
10_1101-2020_10_26_351783	546	127	)	)	-RRB-
10_1101-2020_10_26_351783	546	128	(	(	-LRB-
10_1101-2020_10_26_351783	546	129	P	p	NN
10_1101-2020_10_26_351783	546	130	=	=	SYM
10_1101-2020_10_26_351783	546	131	1.95	1.95	CD
10_1101-2020_10_26_351783	546	132	x	x	SYM
10_1101-2020_10_26_351783	546	133	10	10	CD
10_1101-2020_10_26_351783	546	134	-8	-8	.
10_1101-2020_10_26_351783	546	135	)	)	-RRB-
10_1101-2020_10_26_351783	546	136	(	(	-LRB-
10_1101-2020_10_26_351783	546	137	P	p	NN
10_1101-2020_10_26_351783	546	138	=	=	SYM
10_1101-2020_10_26_351783	546	139	8.76	8.76	CD
10_1101-2020_10_26_351783	546	140	x	x	SYM
10_1101-2020_10_26_351783	546	141	10	10	CD
10_1101-2020_10_26_351783	546	142	-9	-9	CD
10_1101-2020_10_26_351783	546	143	)	)	-RRB-
10_1101-2020_10_26_351783	546	144	Diseas	Diseas	NNP
10_1101-2020_10_26_351783	546	145	e	e	NNP
10_1101-2020_10_26_351783	546	146	Type	Type	NNP
10_1101-2020_10_26_351783	546	147	Cell	Cell	NNP
10_1101-2020_10_26_351783	546	148	Type	Type	NNP
10_1101-2020_10_26_351783	546	149	Mod	Mod	NNP
10_1101-2020_10_26_351783	546	150	.	.	.
10_1101-2020_10_26_351783	547	1	Disco	Disco	NNP
10_1101-2020_10_26_351783	547	2	v.	v.	CC
10_1101-2020_10_26_351783	547	3	MCODE	MCODE	NNP
10_1101-2020_10_26_351783	547	4	Correl	Correl	NNP
10_1101-2020_10_26_351783	547	5	.	.	.
10_1101-2020_10_26_351783	548	1	Clique	Clique	NNP
10_1101-2020_10_26_351783	548	2	Clique	Clique	NNP
10_1101-2020_10_26_351783	548	3	SuM	SuM	VBD
10_1101-2020_10_26_351783	548	4	WGCNA	wgcna	NN
10_1101-2020_10_26_351783	548	5	MODA	moda	NN
10_1101-2020_10_26_351783	548	6	Di	Di	NNP
10_1101-2020_10_26_351783	548	7	�	�	NNP
10_1101-2020_10_26_351783	548	8	�	�	NNP
10_1101-2020_10_26_351783	548	9	CoEx	CoEx	NNP
10_1101-2020_10_26_351783	548	10	DIAMOnD	diamond	NN
10_1101-2020_10_26_351783	548	11	T1	T1	NNP
10_1101-2020_10_26_351783	548	12	2	2	CD
10_1101-2020_10_26_351783	548	13	4	4	CD
10_1101-2020_10_26_351783	548	14	6	6	CD
10_1101-2020_10_26_351783	548	15	8	8	CD
10_1101-2020_10_26_351783	548	16	0	0	CD
10_1101-2020_10_26_351783	548	17	α	α	NN
10_1101-2020_10_26_351783	548	18	=	=	NNS
10_1101-2020_10_26_351783	548	19	.05	.05	CD
10_1101-2020_10_26_351783	548	20	0	0	CD
10_1101-2020_10_26_351783	548	21	2	2	CD
10_1101-2020_10_26_351783	548	22	4	4	CD
10_1101-2020_10_26_351783	548	23	6	6	CD
10_1101-2020_10_26_351783	548	24	8	8	CD
10_1101-2020_10_26_351783	548	25	α	α	NN
10_1101-2020_10_26_351783	548	26	=	=	NNS
10_1101-2020_10_26_351783	548	27	.05	.05	CD
10_1101-2020_10_26_351783	548	28	11.5	11.5	CD
10_1101-2020_10_26_351783	548	29	-log10P	-log10p	JJ
10_1101-2020_10_26_351783	548	30	Disease	Disease	NNP
10_1101-2020_10_26_351783	548	31	Type	Type	NNP
10_1101-2020_10_26_351783	548	32	Cell	Cell	NNP
10_1101-2020_10_26_351783	548	33	Type	Type	NNP
10_1101-2020_10_26_351783	548	34	Mod	Mod	NNP
10_1101-2020_10_26_351783	548	35	.	.	.
10_1101-2020_10_26_351783	549	1	Disco	Disco	NNP
10_1101-2020_10_26_351783	549	2	v.	v.	CC
10_1101-2020_10_26_351783	549	3	MCODE	MCODE	NNP
10_1101-2020_10_26_351783	549	4	Correl	Correl	NNP
10_1101-2020_10_26_351783	549	5	.	.	.
10_1101-2020_10_26_351783	550	1	Cliq	Cliq	NNP
10_1101-2020_10_26_351783	550	2	ue	ue	NNP
10_1101-2020_10_26_351783	550	3	Clique	Clique	NNP
10_1101-2020_10_26_351783	550	4	SuM	SuM	VBD
10_1101-2020_10_26_351783	550	5	WGCNA	wgcna	NN
10_1101-2020_10_26_351783	550	6	MODA	moda	NN
10_1101-2020_10_26_351783	550	7	Di	Di	NNP
10_1101-2020_10_26_351783	550	8	�	�	NNP
10_1101-2020_10_26_351783	550	9	�	�	NNP
10_1101-2020_10_26_351783	550	10	CoEx	CoEx	NNP
10_1101-2020_10_26_351783	550	11	DIAMOnD	diamond	NN
10_1101-2020_10_26_351783	550	12	α	α	NNP
10_1101-2020_10_26_351783	550	13	=	=	SYM
10_1101-2020_10_26_351783	550	14	.05	.05	CD
10_1101-2020_10_26_351783	550	15	0	0	CD
10_1101-2020_10_26_351783	550	16	2	2	CD
10_1101-2020_10_26_351783	550	17	4	4	CD
10_1101-2020_10_26_351783	550	18	6	6	CD
10_1101-2020_10_26_351783	550	19	8	8	CD
10_1101-2020_10_26_351783	550	20	-log10P	-log10p	NN
10_1101-2020_10_26_351783	550	21	T2	t2	NN
10_1101-2020_10_26_351783	550	22	T3	T3	NNP
10_1101-2020_10_26_351783	550	23	T4	T4	NNP
10_1101-2020_10_26_351783	550	24	T5	T5	NNP
10_1101-2020_10_26_351783	550	25	T6	T6	NNP
10_1101-2020_10_26_351783	550	26	T7	t7	NN
10_1101-2020_10_26_351783	550	27	T8	t8	NN
10_1101-2020_10_26_351783	550	28	T9	T9	,
10_1101-2020_10_26_351783	550	29	T10	T10	NNP
10_1101-2020_10_26_351783	550	30	T11	T11	NNP
10_1101-2020_10_26_351783	550	31	M1	M1	NNP
10_1101-2020_10_26_351783	550	32	M2	M2	NNP
10_1101-2020_10_26_351783	550	33	M3	M3	NNP
10_1101-2020_10_26_351783	550	34	M4	M4	NNP
10_1101-2020_10_26_351783	550	35	M5	M5	NNP
10_1101-2020_10_26_351783	550	36	M6	M6	NNP
10_1101-2020_10_26_351783	550	37	M7	M7	NNP
10_1101-2020_10_26_351783	550	38	M8	M8	NNP
10_1101-2020_10_26_351783	550	39	M9	M9	NNP
10_1101-2020_10_26_351783	550	40	NA	NA	NNP
10_1101-2020_10_26_351783	550	41	NA	NA	NNP
10_1101-2020_10_26_351783	550	42	NA	NA	NNP
10_1101-2020_10_26_351783	550	43	NANANA	nanana	NN
10_1101-2020_10_26_351783	550	44	NA	na	NN
10_1101-2020_10_26_351783	550	45	NA	na	IN
10_1101-2020_10_26_351783	550	46	NA	NA	NNP
10_1101-2020_10_26_351783	550	47	NA	NA	NNP
10_1101-2020_10_26_351783	550	48	NA	NA	NNP
10_1101-2020_10_26_351783	550	49	NA	NA	NNP
10_1101-2020_10_26_351783	550	50	NA	NA	NNP
10_1101-2020_10_26_351783	550	51	NA	NA	NNP
10_1101-2020_10_26_351783	550	52	NA	NA	NNP
10_1101-2020_10_26_351783	550	53	NA	NA	NNP
10_1101-2020_10_26_351783	550	54	α	α	NN
10_1101-2020_10_26_351783	550	55	=	=	NNS
10_1101-2020_10_26_351783	550	56	.05	.05	NFP
10_1101-2020_10_26_351783	550	57	0	0	CD
10_1101-2020_10_26_351783	550	58	2	2	CD
10_1101-2020_10_26_351783	550	59	4	4	CD
10_1101-2020_10_26_351783	550	60	6	6	CD
10_1101-2020_10_26_351783	550	61	8	8	CD
10_1101-2020_10_26_351783	550	62	-l	-l	NFP
10_1101-2020_10_26_351783	550	63	o	o	NN
10_1101-2020_10_26_351783	550	64	g	g	NN
10_1101-2020_10_26_351783	550	65	1	1	CD
10_1101-2020_10_26_351783	550	66	0	0	CD
10_1101-2020_10_26_351783	550	67	P	P	NNP
10_1101-2020_10_26_351783	550	68	-l	-l	:
10_1101-2020_10_26_351783	550	69	og	og	NNP
10_1101-2020_10_26_351783	550	70	1	1	CD
10_1101-2020_10_26_351783	550	71	0	0	CD
10_1101-2020_10_26_351783	550	72	P	p	NN
10_1101-2020_10_26_351783	550	73	(	(	-LRB-
10_1101-2020_10_26_351783	550	74	which	which	WDT
10_1101-2020_10_26_351783	550	75	was	be	VBD
10_1101-2020_10_26_351783	550	76	not	not	RB
10_1101-2020_10_26_351783	550	77	certified	certify	VBN
10_1101-2020_10_26_351783	550	78	by	by	IN
10_1101-2020_10_26_351783	550	79	peer	peer	NN
10_1101-2020_10_26_351783	550	80	review	review	NN
10_1101-2020_10_26_351783	550	81	)	)	-RRB-
10_1101-2020_10_26_351783	550	82	is	be	VBZ
10_1101-2020_10_26_351783	550	83	the	the	DT
10_1101-2020_10_26_351783	550	84	author	author	NN
10_1101-2020_10_26_351783	550	85	/	/	SYM
10_1101-2020_10_26_351783	550	86	funder	funder	NN
10_1101-2020_10_26_351783	550	87	.	.	.
10_1101-2020_10_26_351783	551	1	All	all	DT
10_1101-2020_10_26_351783	551	2	rights	right	NNS
10_1101-2020_10_26_351783	551	3	reserved	reserve	VBD
10_1101-2020_10_26_351783	551	4	.	.	.
10_1101-2020_10_26_351783	552	1	No	no	DT
10_1101-2020_10_26_351783	552	2	reuse	reuse	NN
10_1101-2020_10_26_351783	552	3	allowed	allow	VBN
10_1101-2020_10_26_351783	552	4	without	without	IN
10_1101-2020_10_26_351783	552	5	permission	permission	NN
10_1101-2020_10_26_351783	552	6	.	.	.
10_1101-2020_10_26_351783	553	1	The	the	DT
10_1101-2020_10_26_351783	553	2	copyright	copyright	NN
10_1101-2020_10_26_351783	553	3	holder	holder	NN
10_1101-2020_10_26_351783	553	4	for	for	IN
10_1101-2020_10_26_351783	553	5	this	this	DT
10_1101-2020_10_26_351783	553	6	preprintthis	preprintthis	NN
10_1101-2020_10_26_351783	553	7	version	version	NN
10_1101-2020_10_26_351783	553	8	posted	post	VBD
10_1101-2020_10_26_351783	553	9	January	January	NNP
10_1101-2020_10_26_351783	553	10	6	6	CD
10_1101-2020_10_26_351783	553	11	,	,	,
10_1101-2020_10_26_351783	553	12	2021	2021	CD
10_1101-2020_10_26_351783	553	13	.	.	.
10_1101-2020_10_26_351783	553	14	;	;	:
10_1101-2020_10_26_351783	553	15	https://doi.org/10.1101/2020.10.26.351783doi	https://doi.org/10.1101/2020.10.26.351783doi	ADD
10_1101-2020_10_26_351783	553	16	:	:	:
10_1101-2020_10_26_351783	553	17	bioRxiv	biorxiv	IN
10_1101-2020_10_26_351783	553	18	preprint	preprint	NN
10_1101-2020_10_26_351783	553	19	https://doi.org/10.1101/2020.10.26.351783	https://doi.org/10.1101/2020.10.26.351783	NNP
10_1101-2020_10_26_351783	553	20	32	32	CD
10_1101-2020_10_26_351783	553	21	Figure	figure	NN
10_1101-2020_10_26_351783	553	22	5	5	CD
10_1101-2020_10_26_351783	553	23	.	.	.
10_1101-2020_10_26_351783	554	1	Risk	risk	NN
10_1101-2020_10_26_351783	554	2	factor	factor	NN
10_1101-2020_10_26_351783	554	3	enrichment	enrichment	NN
10_1101-2020_10_26_351783	554	4	and	and	CC
10_1101-2020_10_26_351783	554	5	network	network	NN
10_1101-2020_10_26_351783	554	6	visualization	visualization	NN
10_1101-2020_10_26_351783	554	7	of	of	IN
10_1101-2020_10_26_351783	554	8	the	the	DT
10_1101-2020_10_26_351783	554	9	MS	MS	NNP
10_1101-2020_10_26_351783	554	10	multi	multi	JJ
10_1101-2020_10_26_351783	554	11	-	-	JJ
10_1101-2020_10_26_351783	554	12	omic	omic	JJ
10_1101-2020_10_26_351783	554	13	module	module	NN
10_1101-2020_10_26_351783	554	14	.	.	.
10_1101-2020_10_26_351783	555	1	(	(	-LRB-
10_1101-2020_10_26_351783	555	2	a	a	LS
10_1101-2020_10_26_351783	555	3	)	)	-RRB-
10_1101-2020_10_26_351783	555	4	Evidence	evidence	NN
10_1101-2020_10_26_351783	555	5	levels	level	NNS
10_1101-2020_10_26_351783	555	6	and	and	CC
10_1101-2020_10_26_351783	555	7	effect	effect	NN
10_1101-2020_10_26_351783	555	8	on	on	IN
10_1101-2020_10_26_351783	555	9	MS	MS	NNP
10_1101-2020_10_26_351783	555	10	of	of	IN
10_1101-2020_10_26_351783	555	11	the	the	DT
10_1101-2020_10_26_351783	555	12	risk	risk	NN
10_1101-2020_10_26_351783	555	13	factor	factor	NN
10_1101-2020_10_26_351783	555	14	.	.	.
10_1101-2020_10_26_351783	556	1	�	�	NNP
10_1101-2020_10_26_351783	556	2	(	(	-LRB-
10_1101-2020_10_26_351783	556	3	b	b	LS
10_1101-2020_10_26_351783	556	4	)	)	-RRB-
10_1101-2020_10_26_351783	556	5	Enrichment	Enrichment	NNP
10_1101-2020_10_26_351783	556	6	overlap	overlap	NN
10_1101-2020_10_26_351783	556	7	of	of	IN
10_1101-2020_10_26_351783	556	8	multi	multi	JJ
10_1101-2020_10_26_351783	556	9	-	-	JJ
10_1101-2020_10_26_351783	556	10	omic	omic	JJ
10_1101-2020_10_26_351783	556	11	MS	MS	NNP
10_1101-2020_10_26_351783	556	12	DYNC1H1	DYNC1H1	NNP
10_1101-2020_10_26_351783	556	13	JUN	JUN	NNP
10_1101-2020_10_26_351783	556	14	MAPK9	MAPK9	.
10_1101-2020_10_26_351783	556	15	MAPK8	MAPK8	NNP
10_1101-2020_10_26_351783	556	16	PRKCA	PRKCA	NNP
10_1101-2020_10_26_351783	556	17	PRKCE	prkce	NN
10_1101-2020_10_26_351783	556	18	MAPK11	MAPK11	NNS
10_1101-2020_10_26_351783	556	19	LCP2	lcp2	NN
10_1101-2020_10_26_351783	556	20	RHOA	rhoa	NN
10_1101-2020_10_26_351783	556	21	DYNLL1	dynll1	VBP
10_1101-2020_10_26_351783	556	22	GRAP2	GRAP2	''
10_1101-2020_10_26_351783	556	23	BCL6	BCL6	NNP
10_1101-2020_10_26_351783	556	24	DNM3	DNM3	NNP
10_1101-2020_10_26_351783	556	25	DNM1	DNM1	NNP
10_1101-2020_10_26_351783	556	26	PRKACB	PRKACB	NNP
10_1101-2020_10_26_351783	556	27	CASP3	CASP3	NNP
10_1101-2020_10_26_351783	556	28	BCL2	BCL2	NNP
10_1101-2020_10_26_351783	556	29	NRIP1	nrip1	NN
10_1101-2020_10_26_351783	556	30	DNM2	DNM2	NNP
10_1101-2020_10_26_351783	556	31	BCL2L11	bcl2l11	VB
10_1101-2020_10_26_351783	556	32	PRKACA	PRKACA	NNP
10_1101-2020_10_26_351783	556	33	PTEN	PTEN	NNP
10_1101-2020_10_26_351783	556	34	ATF2	ATF2	NNP
10_1101-2020_10_26_351783	556	35	PRKCI	PRKCI	NNP
10_1101-2020_10_26_351783	556	36	BID	BID	NNP
10_1101-2020_10_26_351783	556	37	RAC1	RAC1	NNP
10_1101-2020_10_26_351783	556	38	RAC2	RAC2	NNP
10_1101-2020_10_26_351783	556	39	RASA1	RASA1	VBZ
10_1101-2020_10_26_351783	556	40	NRAS	NRAS	NNP
10_1101-2020_10_26_351783	556	41	SOS1	SOS1	NNP
10_1101-2020_10_26_351783	556	42	PIK3CA	pik3ca	JJ
10_1101-2020_10_26_351783	556	43	HRAS	HRAS	NNP
10_1101-2020_10_26_351783	556	44	CASP8	CASP8	.
10_1101-2020_10_26_351783	556	45	CDC42	cdc42	CD
10_1101-2020_10_26_351783	556	46	PRKCZ	PRKCZ	VBN
10_1101-2020_10_26_351783	556	47	PARD6A	PARD6A	NNP
10_1101-2020_10_26_351783	556	48	MET	MET	NNP
10_1101-2020_10_26_351783	556	49	PLCG1	PLCG1	NNS
10_1101-2020_10_26_351783	556	50	IRS1	IRS1	NNP
10_1101-2020_10_26_351783	556	51	PTK2	PTK2	NNP
10_1101-2020_10_26_351783	556	52	PGR	PGR	NNP
10_1101-2020_10_26_351783	556	53	KRAS	KRAS	NNP
10_1101-2020_10_26_351783	556	54	RET	RET	NNP
10_1101-2020_10_26_351783	556	55	HGF	HGF	NNP
10_1101-2020_10_26_351783	556	56	PIK3CB	pik3cb	NN
10_1101-2020_10_26_351783	556	57	GAB1	GAB1	NNP
10_1101-2020_10_26_351783	556	58	VAV1	VAV1	NNP
10_1101-2020_10_26_351783	556	59	GRB2	GRB2	NNP
10_1101-2020_10_26_351783	556	60	ERBB2	ERBB2	NNP
10_1101-2020_10_26_351783	556	61	HCK	HCK	NNP
10_1101-2020_10_26_351783	556	62	PIK3CD	pik3cd	NN
10_1101-2020_10_26_351783	556	63	CRKL	CRKL	NNS
10_1101-2020_10_26_351783	556	64	PIK3R2	pik3r2	RB
10_1101-2020_10_26_351783	556	65	CARM1	CARM1	:
10_1101-2020_10_26_351783	556	66	IGF1	IGF1	NNP
10_1101-2020_10_26_351783	556	67	PTK2B	ptk2b	CD
10_1101-2020_10_26_351783	556	68	KDR	KDR	NNP
10_1101-2020_10_26_351783	556	69	VEGFA	VEGFA	NNP
10_1101-2020_10_26_351783	556	70	PXN	PXN	NNP
10_1101-2020_10_26_351783	556	71	EDN1	EDN1	NNPS
10_1101-2020_10_26_351783	556	72	CBL	CBL	NNP
10_1101-2020_10_26_351783	556	73	BCAR1	bcar1	VBP
10_1101-2020_10_26_351783	556	74	APP	APP	NNP
10_1101-2020_10_26_351783	556	75	SH3GL2	SH3GL2	NNP
10_1101-2020_10_26_351783	556	76	IQGAP1	IQGAP1	''
10_1101-2020_10_26_351783	556	77	SHC1	SHC1	NNP
10_1101-2020_10_26_351783	556	78	BDNF	BDNF	NNP
10_1101-2020_10_26_351783	556	79	NGF	NGF	NNP
10_1101-2020_10_26_351783	556	80	NTRK1	NTRK1	NNP
10_1101-2020_10_26_351783	556	81	PTPN6	PTPN6	NNP
10_1101-2020_10_26_351783	556	82	EGFR	EGFR	NNP
10_1101-2020_10_26_351783	556	83	INS	INS	NNP
10_1101-2020_10_26_351783	556	84	GNB1	GNB1	NNP
10_1101-2020_10_26_351783	556	85	GNG2	GNG2	NNP
10_1101-2020_10_26_351783	556	86	ARID1A	arid1a	CD
10_1101-2020_10_26_351783	556	87	TRIM25	trim25	NN
10_1101-2020_10_26_351783	556	88	GNAI1	GNAI1	NNS
10_1101-2020_10_26_351783	556	89	AR	AR	NNP
10_1101-2020_10_26_351783	556	90	PIK3R3	PIK3R3	:
10_1101-2020_10_26_351783	556	91	PIK3R1	pik3r1	ADD
10_1101-2020_10_26_351783	556	92	PTPRJ	PTPRJ	NNP
10_1101-2020_10_26_351783	556	93	SP1	SP1	NNP
10_1101-2020_10_26_351783	556	94	INPP5B	INPP5B	NNP
10_1101-2020_10_26_351783	556	95	TNF	TNF	NNP
10_1101-2020_10_26_351783	556	96	CTNNB1	CTNNB1	VBZ
10_1101-2020_10_26_351783	556	97	NCAM1	NCAM1	NNP
10_1101-2020_10_26_351783	556	98	CDH1	CDH1	NNP
10_1101-2020_10_26_351783	556	99	SPP1	SPP1	NNPS
10_1101-2020_10_26_351783	556	100	SEC13	SEC13	NNP
10_1101-2020_10_26_351783	556	101	CSK	CSK	NNP
10_1101-2020_10_26_351783	556	102	TLN1	TLN1	NNP
10_1101-2020_10_26_351783	556	103	RAP1B	RAP1B	-LRB-
10_1101-2020_10_26_351783	556	104	ABL	ABL	NNP
10_1101-2020_10_26_351783	556	105	1SRC	1src	NN
10_1101-2020_10_26_351783	556	106	ITGB3	itgb3	NN
10_1101-2020_10_26_351783	556	107	PTPN11	ptpn11	UH
10_1101-2020_10_26_351783	556	108	EGF	EGF	NNP
10_1101-2020_10_26_351783	556	109	IT	IT	NNP
10_1101-2020_10_26_351783	556	110	GB1	GB1	NNP
10_1101-2020_10_26_351783	556	111	ITGAV	ITGAV	NNP
10_1101-2020_10_26_351783	556	112	SYNJ2	synj2	JJ
10_1101-2020_10_26_351783	556	113	CD7	CD7	NNP
10_1101-2020_10_26_351783	556	114	4	4	CD
10_1101-2020_10_26_351783	556	115	HLA	HLA	NNP
10_1101-2020_10_26_351783	556	116	-	-	HYPH
10_1101-2020_10_26_351783	556	117	E	e	NN
10_1101-2020_10_26_351783	556	118	CLTA	CLTA	NNP
10_1101-2020_10_26_351783	556	119	CD4	CD4	NNP
10_1101-2020_10_26_351783	556	120	HLA	HLA	NNP
10_1101-2020_10_26_351783	556	121	-	-	HYPH
10_1101-2020_10_26_351783	556	122	DPB1	DPB1	NNP
10_1101-2020_10_26_351783	556	123	HLA	HLA	NNP
10_1101-2020_10_26_351783	556	124	-	-	,
10_1101-2020_10_26_351783	556	125	A	a	NN
10_1101-2020_10_26_351783	556	126	PTPN22	PTPN22	NNP
10_1101-2020_10_26_351783	556	127	HLA	HLA	NNP
10_1101-2020_10_26_351783	556	128	-	-	HYPH
10_1101-2020_10_26_351783	556	129	DRA	DRA	NNP
10_1101-2020_10_26_351783	556	130	IL	IL	NNP
10_1101-2020_10_26_351783	556	131	10	10	CD
10_1101-2020_10_26_351783	556	132	MMP9	MMP9	NNP
10_1101-2020_10_26_351783	556	133	PIP5K1B	pip5k1b	RB
10_1101-2020_10_26_351783	556	134	CXCR4	cxcr4	JJ
10_1101-2020_10_26_351783	556	135	CXCL12	cxcl12	NN
10_1101-2020_10_26_351783	556	136	ICAM1	ICAM1	''
10_1101-2020_10_26_351783	556	137	LCKHLA	LCKHLA	NNP
10_1101-2020_10_26_351783	556	138	-	-	HYPH
10_1101-2020_10_26_351783	556	139	DRB1	DRB1	NNP
10_1101-2020_10_26_351783	556	140	AP2M1	AP2M1	NNP
10_1101-2020_10_26_351783	556	141	AP2B1	AP2B1	NNP
10_1101-2020_10_26_351783	556	142	FCGR1A	FCGR1A	NNP
10_1101-2020_10_26_351783	556	143	AP1M1	AP1M1	NNP
10_1101-2020_10_26_351783	556	144	MAPK14	MAPK14	NNP
10_1101-2020_10_26_351783	556	145	VWF	VWF	NNP
10_1101-2020_10_26_351783	556	146	IRF7	IRF7	NNP
10_1101-2020_10_26_351783	556	147	IRF1	IRF1	NNP
10_1101-2020_10_26_351783	556	148	IRF4	IRF4	NNP
10_1101-2020_10_26_351783	556	149	IL4	IL4	NNP
10_1101-2020_10_26_351783	556	150	IL	IL	NNP
10_1101-2020_10_26_351783	556	151	6	6	CD
10_1101-2020_10_26_351783	556	152	IFNG	IFNG	NNP
10_1101-2020_10_26_351783	556	153	AKT3	AKT3	NNP
10_1101-2020_10_26_351783	556	154	A	A	NNP
10_1101-2020_10_26_351783	556	155	P2	p2	NN
10_1101-2020_10_26_351783	556	156	A2	A2	NNP
10_1101-2020_10_26_351783	556	157	HSP90AA1	HSP90AA1	NNP
10_1101-2020_10_26_351783	556	158	CD3D	cd3d	JJ
10_1101-2020_10_26_351783	556	159	PPP2R1A	PPP2R1A	NNP
10_1101-2020_10_26_351783	556	160	GSK3B	gsk3b	CD
10_1101-2020_10_26_351783	556	161	PPP2CA	ppp2ca	NN
10_1101-2020_10_26_351783	556	162	FGG	FGG	NNP
10_1101-2020_10_26_351783	556	163	EPS15L1	EPS15L1	VBZ
10_1101-2020_10_26_351783	556	164	FGF2	FGF2	NNP
10_1101-2020_10_26_351783	556	165	PTPRC	PTPRC	NNP
10_1101-2020_10_26_351783	556	166	CD3	cd3	NN
10_1101-2020_10_26_351783	556	167	G	g	NN
10_1101-2020_10_26_351783	556	168	HSP90AB1	hsp90ab1	ADD
10_1101-2020_10_26_351783	556	169	EPHA2	EPHA2	NNP
10_1101-2020_10_26_351783	556	170	F	F	NNP
10_1101-2020_10_26_351783	556	171	N1	N1	NNP
10_1101-2020_10_26_351783	556	172	CLTC	CLTC	NNP
10_1101-2020_10_26_351783	556	173	PIP5K1A	PIP5K1A	NNP
10_1101-2020_10_26_351783	556	174	VCAM1	vcam1	VB
10_1101-2020_10_26_351783	556	175	FYN	FYN	NNP
10_1101-2020_10_26_351783	556	176	ESR1	ESR1	NNP
10_1101-2020_10_26_351783	556	177	TGFB1	tgfb1	NN
10_1101-2020_10_26_351783	556	178	ITGB2	ITGB2	NNP
10_1101-2020_10_26_351783	556	179	CD8	CD8	NNP
10_1101-2020_10_26_351783	556	180	6	6	CD
10_1101-2020_10_26_351783	556	181	NR3C1	NR3C1	NNP
10_1101-2020_10_26_351783	556	182	CD80	CD80	NNP
10_1101-2020_10_26_351783	556	183	CD3E	CD3E	NNP
10_1101-2020_10_26_351783	556	184	AP2A1	AP2A1	NNP
10_1101-2020_10_26_351783	556	185	RUNX1	runx1	VBP
10_1101-2020_10_26_351783	556	186	CD28	CD28	NNP
10_1101-2020_10_26_351783	556	187	CD4	CD4	NNP
10_1101-2020_10_26_351783	556	188	4	4	CD
10_1101-2020_10_26_351783	556	189	CEBPB	cebpb	NN
10_1101-2020_10_26_351783	556	190	AP2S1	ap2s1	NN
10_1101-2020_10_26_351783	556	191	NFKB1	nfkb1	NN
10_1101-2020_10_26_351783	556	192	HDAC1	hdac1	UH
10_1101-2020_10_26_351783	556	193	KIT	KIT	NNP
10_1101-2020_10_26_351783	556	194	CDK4	CDK4	NNP
10_1101-2020_10_26_351783	556	195	CCNA1	ccna1	POS
10_1101-2020_10_26_351783	556	196	UBE2I	ube2i	CD
10_1101-2020_10_26_351783	556	197	PCNA	PCNA	NNP
10_1101-2020_10_26_351783	556	198	CCND1	ccnd1	NN
10_1101-2020_10_26_351783	556	199	RELA	rela	NN
10_1101-2020_10_26_351783	556	200	STAT5A	stat5a	NN
10_1101-2020_10_26_351783	556	201	PRKCD	PRKCD	NNS
10_1101-2020_10_26_351783	556	202	PRKCQ	PRKCQ	NNP
10_1101-2020_10_26_351783	556	203	ZAP70	zap70	NN
10_1101-2020_10_26_351783	556	204	RAF1	raf1	CD
10_1101-2020_10_26_351783	556	205	YWHAB	YWHAB	NNP
10_1101-2020_10_26_351783	556	206	AKT1	AKT1	NNP
10_1101-2020_10_26_351783	556	207	CD24	CD24	NNP
10_1101-2020_10_26_351783	556	208	7	7	CD
10_1101-2020_10_26_351783	556	209	RAP1A	rap1a	CD
10_1101-2020_10_26_351783	556	210	MAPK1	MAPK1	NNS
10_1101-2020_10_26_351783	556	211	MAPK3	mapk3	NN
10_1101-2020_10_26_351783	556	212	PTAFR	PTAFR	NNP
10_1101-2020_10_26_351783	556	213	RAB7A	rab7a	PRP
10_1101-2020_10_26_351783	556	214	MAP2K1	map2k1	CD
10_1101-2020_10_26_351783	556	215	SMAD4	smad4	UH
10_1101-2020_10_26_351783	556	216	MAP3K5	MAP3K5	:
10_1101-2020_10_26_351783	556	217	CREBBP	crebbp	NN
10_1101-2020_10_26_351783	556	218	SMAD2	smad2	NN
10_1101-2020_10_26_351783	556	219	HMGB1	hmgb1	NN
10_1101-2020_10_26_351783	556	220	NGFR	NGFR	NNP
10_1101-2020_10_26_351783	556	221	DAXX	DAXX	NNP
10_1101-2020_10_26_351783	556	222	AKT2	AKT2	NNP
10_1101-2020_10_26_351783	556	223	PPARG	PPARG	NNP
10_1101-2020_10_26_351783	556	224	TRIM2	trim2	VBP
10_1101-2020_10_26_351783	556	225	4	4	CD
10_1101-2020_10_26_351783	556	226	SMAD3	smad3	CD
10_1101-2020_10_26_351783	556	227	MYC	MYC	NNP
10_1101-2020_10_26_351783	556	228	CTSS	CTSS	NNP
10_1101-2020_10_26_351783	556	229	SIRT1	SIRT1	NNS
10_1101-2020_10_26_351783	556	230	CSF2	CSF2	NNP
10_1101-2020_10_26_351783	556	231	BRCA1	BRCA1	NNP
10_1101-2020_10_26_351783	556	232	SPTBN2	SPTBN2	.
10_1101-2020_10_26_351783	556	233	TP53	tp53	IN
10_1101-2020_10_26_351783	556	234	H2	H2	NNP
10_1101-2020_10_26_351783	556	235	AX	AX	NNP
10_1101-2020_10_26_351783	556	236	SPHK1	SPHK1	NNP
10_1101-2020_10_26_351783	556	237	EP3	EP3	NNP
10_1101-2020_10_26_351783	556	238	00	00	CD
10_1101-2020_10_26_351783	556	239	JAK1	JAK1	NNP
10_1101-2020_10_26_351783	556	240	IRF3	IRF3	NNP
10_1101-2020_10_26_351783	556	241	STAT3	STAT3	:
10_1101-2020_10_26_351783	556	242	STAT1	stat1	NN
10_1101-2020_10_26_351783	556	243	STAT6	STAT6	''
10_1101-2020_10_26_351783	556	244	PAK1	PAK1	NNP
10_1101-2020_10_26_351783	556	245	HIF1A	HIF1A	NNP
10_1101-2020_10_26_351783	556	246	PLCG2PDGFB	PLCG2PDGFB	:
10_1101-2020_10_26_351783	556	247	JAK2	JAK2	NNP
10_1101-2020_10_26_351783	556	248	PDGFRB	PDGFRB	NNP
10_1101-2020_10_26_351783	556	249	CCNE1	ccne1	NN
10_1101-2020_10_26_351783	556	250	RUNX3	RUNX3	VBZ
10_1101-2020_10_26_351783	556	251	RB1	RB1	NNP
10_1101-2020_10_26_351783	556	252	EZH2CDK2	EZH2CDK2	NNP
10_1101-2020_10_26_351783	556	253	Functional	functional	JJ
10_1101-2020_10_26_351783	556	254	Clusters	Clusters	NNPS
10_1101-2020_10_26_351783	556	255	Cell	Cell	NNP
10_1101-2020_10_26_351783	556	256	death	death	NN
10_1101-2020_10_26_351783	556	257	and	and	CC
10_1101-2020_10_26_351783	556	258	apoptosis	apoptosis	NN
10_1101-2020_10_26_351783	556	259	Morphogenesis	Morphogenesis	NNP
10_1101-2020_10_26_351783	556	260	and	and	CC
10_1101-2020_10_26_351783	556	261	neurogenesis	neurogenesis	NN
10_1101-2020_10_26_351783	556	262	Cell	Cell	NNP
10_1101-2020_10_26_351783	556	263	cycle	cycle	NN
10_1101-2020_10_26_351783	556	264	and	and	CC
10_1101-2020_10_26_351783	556	265	proliferation	proliferation	NN
10_1101-2020_10_26_351783	556	266	Chemotaxis	chemotaxi	NNS
10_1101-2020_10_26_351783	556	267	and	and	CC
10_1101-2020_10_26_351783	556	268	cell	cell	NN
10_1101-2020_10_26_351783	556	269	migration	migration	NN
10_1101-2020_10_26_351783	556	270	Response	Response	NNP
10_1101-2020_10_26_351783	556	271	to	to	IN
10_1101-2020_10_26_351783	556	272	hormone	hormone	NN
10_1101-2020_10_26_351783	556	273	stimulus	stimulus	NN
10_1101-2020_10_26_351783	556	274	Leukocyte	Leukocyte	NNP
10_1101-2020_10_26_351783	556	275	activation	activation	NN
10_1101-2020_10_26_351783	556	276	and	and	CC
10_1101-2020_10_26_351783	556	277	di	di	FW
10_1101-2020_10_26_351783	556	278	�	�	NNS
10_1101-2020_10_26_351783	556	279	�	�	,
10_1101-2020_10_26_351783	556	280	erentiation	erentiation	NN
10_1101-2020_10_26_351783	556	281	Node	Node	NNP
10_1101-2020_10_26_351783	556	282	Color	Color	NNP
10_1101-2020_10_26_351783	556	283	Legend	Legend	NNP
10_1101-2020_10_26_351783	556	284	Low	Low	NNP
10_1101-2020_10_26_351783	556	285	sun	sun	NN
10_1101-2020_10_26_351783	556	286	exposure	exposure	NN
10_1101-2020_10_26_351783	556	287	Smoking	Smoking	NNP
10_1101-2020_10_26_351783	556	288	High	High	NNP
10_1101-2020_10_26_351783	556	289	BMI	BMI	NNP
10_1101-2020_10_26_351783	556	290	Alcohol	Alcohol	NNP
10_1101-2020_10_26_351783	556	291	use	use	NN
10_1101-2020_10_26_351783	556	292	EBV	EBV	NNP
10_1101-2020_10_26_351783	556	293	infection	infection	NN
10_1101-2020_10_26_351783	556	294	Associated	Associated	NNP
10_1101-2020_10_26_351783	556	295	with	with	IN
10_1101-2020_10_26_351783	556	296	MS	MS	NNP
10_1101-2020_10_26_351783	556	297	Signif	Signif	NNP
10_1101-2020_10_26_351783	556	298	.	.	.
10_1101-2020_10_26_351783	557	1	enriched	enriched	JJ
10_1101-2020_10_26_351783	557	2	MS	MS	NNP
10_1101-2020_10_26_351783	557	3	risk	risk	NN
10_1101-2020_10_26_351783	557	4	factors	factor	NNS
10_1101-2020_10_26_351783	557	5	Risk	Risk	NNP
10_1101-2020_10_26_351783	557	6	factor	factor	NN
10_1101-2020_10_26_351783	557	7	Evidence	evidence	NN
10_1101-2020_10_26_351783	557	8	E	e	NN
10_1101-2020_10_26_351783	557	9	�	�	NNP
10_1101-2020_10_26_351783	557	10	�	�	ADD
10_1101-2020_10_26_351783	557	11	ect	ect	VBP
10_1101-2020_10_26_351783	557	12	EBV	EBV	NNP
10_1101-2020_10_26_351783	557	13	infection	infection	NN
10_1101-2020_10_26_351783	557	14	Smoking	Smoking	NNP
10_1101-2020_10_26_351783	557	15	Low	Low	NNP
10_1101-2020_10_26_351783	557	16	sun	sun	NN
10_1101-2020_10_26_351783	557	17	exposure	exposure	NN
10_1101-2020_10_26_351783	557	18	Adolescent	adolescent	JJ
10_1101-2020_10_26_351783	557	19	obesity	obesity	NN
10_1101-2020_10_26_351783	557	20	High	high	JJ
10_1101-2020_10_26_351783	557	21	BMI	BMI	NNP
10_1101-2020_10_26_351783	557	22	Night	Night	NNP
10_1101-2020_10_26_351783	557	23	shift	shift	NN
10_1101-2020_10_26_351783	557	24	work	work	NN
10_1101-2020_10_26_351783	557	25	Organic	organic	JJ
10_1101-2020_10_26_351783	557	26	solvent	solvent	NN
10_1101-2020_10_26_351783	557	27	exposure	exposure	NN
10_1101-2020_10_26_351783	557	28	Alcohol	Alcohol	NNP
10_1101-2020_10_26_351783	557	29	consumption	consumption	NN
10_1101-2020_10_26_351783	557	30	Oral	oral	JJ
10_1101-2020_10_26_351783	557	31	tobacco	tobacco	NN
10_1101-2020_10_26_351783	557	32	+	+	CD
10_1101-2020_10_26_351783	557	33	+	+	SYM
10_1101-2020_10_26_351783	557	34	+	+	SYM
10_1101-2020_10_26_351783	557	35	+	+	SYM
10_1101-2020_10_26_351783	557	36	+	+	SYM
10_1101-2020_10_26_351783	557	37	+	+	SYM
10_1101-2020_10_26_351783	557	38	+	+	SYM
10_1101-2020_10_26_351783	557	39	+	+	SYM
10_1101-2020_10_26_351783	557	40	+	+	SYM
10_1101-2020_10_26_351783	557	41	+	+	SYM
10_1101-2020_10_26_351783	557	42	+	+	SYM
10_1101-2020_10_26_351783	557	43	+	+	SYM
10_1101-2020_10_26_351783	557	44	+	+	SYM
10_1101-2020_10_26_351783	557	45	+	+	SYM
10_1101-2020_10_26_351783	557	46	+	+	SYM
10_1101-2020_10_26_351783	557	47	+	+	SYM
10_1101-2020_10_26_351783	557	48	+	+	SYM
10_1101-2020_10_26_351783	557	49	�	�	NN
10_1101-2020_10_26_351783	557	50	Risk	risk	NN
10_1101-2020_10_26_351783	557	51	�	�	NNP
10_1101-2020_10_26_351783	557	52	Risk	Risk	NNP
10_1101-2020_10_26_351783	557	53	�	�	NNP
10_1101-2020_10_26_351783	557	54	Risk	Risk	NNP
10_1101-2020_10_26_351783	557	55	�	�	NNP
10_1101-2020_10_26_351783	557	56	Risk	Risk	NNP
10_1101-2020_10_26_351783	557	57	�	�	NNP
10_1101-2020_10_26_351783	557	58	Risk	Risk	NNP
10_1101-2020_10_26_351783	557	59	�	�	NNP
10_1101-2020_10_26_351783	557	60	Risk	Risk	NNP
10_1101-2020_10_26_351783	557	61	�	�	NNP
10_1101-2020_10_26_351783	557	62	Risk	Risk	NNP
10_1101-2020_10_26_351783	557	63	�	�	NNP
10_1101-2020_10_26_351783	557	64	Risk	Risk	NNP
10_1101-2020_10_26_351783	557	65	a	a	DT
10_1101-2020_10_26_351783	557	66	c	c	NN
10_1101-2020_10_26_351783	557	67	b	b	NN
10_1101-2020_10_26_351783	557	68	Module	Module	NNP
10_1101-2020_10_26_351783	557	69	enrichments	enrichment	NNS
10_1101-2020_10_26_351783	557	70	1	1	CD
10_1101-2020_10_26_351783	557	71	2	2	CD
10_1101-2020_10_26_351783	557	72	3	3	CD
10_1101-2020_10_26_351783	557	73	4	4	CD
10_1101-2020_10_26_351783	557	74	Risk	risk	NN
10_1101-2020_10_26_351783	557	75	factor	factor	NN
10_1101-2020_10_26_351783	557	76	datasets	dataset	VBZ
10_1101-2020_10_26_351783	557	77	-log10	-log10	NNP
10_1101-2020_10_26_351783	557	78	P	p	NN
10_1101-2020_10_26_351783	557	79	α	α	NN
10_1101-2020_10_26_351783	557	80	=	=	NNS
10_1101-2020_10_26_351783	557	81	.05	.05	CD
10_1101-2020_10_26_351783	557	82	1	1	CD
10_1101-2020_10_26_351783	557	83	2	2	CD
10_1101-2020_10_26_351783	557	84	3	3	CD
10_1101-2020_10_26_351783	557	85	4	4	CD
10_1101-2020_10_26_351783	557	86	Validation	validation	NN
10_1101-2020_10_26_351783	557	87	dataset	dataset	NN
10_1101-2020_10_26_351783	557	88	-log10	-log10	NNP
10_1101-2020_10_26_351783	557	89	P	p	NN
10_1101-2020_10_26_351783	557	90	α	α	NN
10_1101-2020_10_26_351783	557	91	=	=	SYM
10_1101-2020_10_26_351783	557	92	.05	.05	NFP
10_1101-2020_10_26_351783	557	93	NA	NA	NNP
10_1101-2020_10_26_351783	557	94	NA	NA	NNP
10_1101-2020_10_26_351783	557	95	NA	NA	NNP
10_1101-2020_10_26_351783	557	96	7.4	7.4	CD
10_1101-2020_10_26_351783	557	97	�	�	NN
10_1101-2020_10_26_351783	557	98	Risk	risk	NN
10_1101-2020_10_26_351783	557	99	(	(	-LRB-
10_1101-2020_10_26_351783	557	100	which	which	WDT
10_1101-2020_10_26_351783	557	101	was	be	VBD
10_1101-2020_10_26_351783	557	102	not	not	RB
10_1101-2020_10_26_351783	557	103	certified	certify	VBN
10_1101-2020_10_26_351783	557	104	by	by	IN
10_1101-2020_10_26_351783	557	105	peer	peer	NN
10_1101-2020_10_26_351783	557	106	review	review	NN
10_1101-2020_10_26_351783	557	107	)	)	-RRB-
10_1101-2020_10_26_351783	557	108	is	be	VBZ
10_1101-2020_10_26_351783	557	109	the	the	DT
10_1101-2020_10_26_351783	557	110	author	author	NN
10_1101-2020_10_26_351783	557	111	/	/	SYM
10_1101-2020_10_26_351783	557	112	funder	funder	NN
10_1101-2020_10_26_351783	557	113	.	.	.
10_1101-2020_10_26_351783	558	1	All	all	DT
10_1101-2020_10_26_351783	558	2	rights	right	NNS
10_1101-2020_10_26_351783	558	3	reserved	reserve	VBD
10_1101-2020_10_26_351783	558	4	.	.	.
10_1101-2020_10_26_351783	559	1	No	no	DT
10_1101-2020_10_26_351783	559	2	reuse	reuse	NN
10_1101-2020_10_26_351783	559	3	allowed	allow	VBN
10_1101-2020_10_26_351783	559	4	without	without	IN
10_1101-2020_10_26_351783	559	5	permission	permission	NN
10_1101-2020_10_26_351783	559	6	.	.	.
10_1101-2020_10_26_351783	560	1	The	the	DT
10_1101-2020_10_26_351783	560	2	copyright	copyright	NN
10_1101-2020_10_26_351783	560	3	holder	holder	NN
10_1101-2020_10_26_351783	560	4	for	for	IN
10_1101-2020_10_26_351783	560	5	this	this	DT
10_1101-2020_10_26_351783	560	6	preprintthis	preprintthis	NN
10_1101-2020_10_26_351783	560	7	version	version	NN
10_1101-2020_10_26_351783	560	8	posted	post	VBD
10_1101-2020_10_26_351783	560	9	January	January	NNP
10_1101-2020_10_26_351783	560	10	6	6	CD
10_1101-2020_10_26_351783	560	11	,	,	,
10_1101-2020_10_26_351783	560	12	2021	2021	CD
10_1101-2020_10_26_351783	560	13	.	.	.
10_1101-2020_10_26_351783	560	14	;	;	:
10_1101-2020_10_26_351783	560	15	https://doi.org/10.1101/2020.10.26.351783doi	https://doi.org/10.1101/2020.10.26.351783doi	ADD
10_1101-2020_10_26_351783	560	16	:	:	:
10_1101-2020_10_26_351783	560	17	bioRxiv	biorxiv	VB
10_1101-2020_10_26_351783	560	18	preprint	preprint	NN
10_1101-2020_10_26_351783	560	19	https://doi.org/10.1101/2020.10.26.351783	https://doi.org/10.1101/2020.10.26.351783	CC
10_1101-2020_10_26_351783	560	20	33	33	CD
10_1101-2020_10_26_351783	560	21	module	module	JJ
10_1101-2020_10_26_351783	560	22	genes	gene	NNS
10_1101-2020_10_26_351783	560	23	in	in	IN
10_1101-2020_10_26_351783	560	24	the	the	DT
10_1101-2020_10_26_351783	560	25	top	top	JJ
10_1101-2020_10_26_351783	560	26	1,000	1,000	CD
10_1101-2020_10_26_351783	560	27	DMGs	dmg	NNS
10_1101-2020_10_26_351783	560	28	in	in	IN
10_1101-2020_10_26_351783	560	29	risk	risk	NN
10_1101-2020_10_26_351783	560	30	factor	factor	NN
10_1101-2020_10_26_351783	560	31	datasets	dataset	NNS
10_1101-2020_10_26_351783	560	32	and	and	CC
10_1101-2020_10_26_351783	560	33	independent	independent	JJ
10_1101-2020_10_26_351783	560	34	risk	risk	NN
10_1101-2020_10_26_351783	560	35	factor	factor	NN
10_1101-2020_10_26_351783	560	36	methylation	methylation	NN
10_1101-2020_10_26_351783	560	37	dataset	dataset	NN
10_1101-2020_10_26_351783	560	38	(	(	-LRB-
10_1101-2020_10_26_351783	560	39	see	see	VB
10_1101-2020_10_26_351783	560	40	Methods	method	NNS
10_1101-2020_10_26_351783	560	41	)	)	-RRB-
10_1101-2020_10_26_351783	560	42	shown	show	VBN
10_1101-2020_10_26_351783	560	43	as	as	IN
10_1101-2020_10_26_351783	560	44	Fisher	Fisher	NNP
10_1101-2020_10_26_351783	560	45	exact	exact	JJ
10_1101-2020_10_26_351783	560	46	test	test	NN
10_1101-2020_10_26_351783	560	47	P	p	NN
10_1101-2020_10_26_351783	560	48	-	-	HYPH
10_1101-2020_10_26_351783	560	49	values	value	NNS
10_1101-2020_10_26_351783	560	50	(	(	-LRB-
10_1101-2020_10_26_351783	560	51	threshold	threshold	NN
10_1101-2020_10_26_351783	560	52	α=0.05	α=0.05	NNP
10_1101-2020_10_26_351783	560	53	)	)	-RRB-
10_1101-2020_10_26_351783	560	54	.	.	.
10_1101-2020_10_26_351783	561	1	(	(	-LRB-
10_1101-2020_10_26_351783	561	2	c	c	NN
10_1101-2020_10_26_351783	561	3	)	)	-RRB-
10_1101-2020_10_26_351783	561	4	Visualization	visualization	NN
10_1101-2020_10_26_351783	561	5	of	of	IN
10_1101-2020_10_26_351783	561	6	the	the	DT
10_1101-2020_10_26_351783	561	7	module	module	NN
10_1101-2020_10_26_351783	561	8	.	.	.
10_1101-2020_10_26_351783	562	1	Nodes	node	NNS
10_1101-2020_10_26_351783	562	2	(	(	-LRB-
10_1101-2020_10_26_351783	562	3	module	module	JJ
10_1101-2020_10_26_351783	562	4	genes	gene	NNS
10_1101-2020_10_26_351783	562	5	)	)	-RRB-
10_1101-2020_10_26_351783	562	6	are	be	VBP
10_1101-2020_10_26_351783	562	7	arranged	arrange	VBN
10_1101-2020_10_26_351783	562	8	in	in	IN
10_1101-2020_10_26_351783	562	9	functional	functional	JJ
10_1101-2020_10_26_351783	562	10	clusters	cluster	NNS
10_1101-2020_10_26_351783	562	11	according	accord	VBG
10_1101-2020_10_26_351783	562	12	to	to	IN
10_1101-2020_10_26_351783	562	13	their	-PRON-	PRP$
10_1101-2020_10_26_351783	562	14	overrepresented	overrepresente	VBN
10_1101-2020_10_26_351783	562	15	GO	GO	NNP
10_1101-2020_10_26_351783	562	16	terms	term	NNS
10_1101-2020_10_26_351783	562	17	.	.	.
10_1101-2020_10_26_351783	563	1	Genes	gene	NNS
10_1101-2020_10_26_351783	563	2	with	with	IN
10_1101-2020_10_26_351783	563	3	a	a	DT
10_1101-2020_10_26_351783	563	4	known	know	VBN
10_1101-2020_10_26_351783	563	5	association	association	NN
10_1101-2020_10_26_351783	563	6	to	to	IN
10_1101-2020_10_26_351783	563	7	MS	MS	NNP
10_1101-2020_10_26_351783	563	8	are	be	VBP
10_1101-2020_10_26_351783	563	9	marked	mark	VBN
10_1101-2020_10_26_351783	563	10	with	with	IN
10_1101-2020_10_26_351783	563	11	a	a	DT
10_1101-2020_10_26_351783	563	12	blue	blue	JJ
10_1101-2020_10_26_351783	563	13	circle	circle	NN
10_1101-2020_10_26_351783	563	14	.	.	.
10_1101-2020_10_26_351783	564	1	Node	Node	NNP
10_1101-2020_10_26_351783	564	2	colors	color	NNS
10_1101-2020_10_26_351783	564	3	display	display	VBP
10_1101-2020_10_26_351783	564	4	the	the	DT
10_1101-2020_10_26_351783	564	5	associations	association	NNS
10_1101-2020_10_26_351783	564	6	to	to	IN
10_1101-2020_10_26_351783	564	7	an	an	DT
10_1101-2020_10_26_351783	564	8	MS	MS	NNP
10_1101-2020_10_26_351783	564	9	risk	risk	NN
10_1101-2020_10_26_351783	564	10	factor	factor	NN
10_1101-2020_10_26_351783	564	11	for	for	IN
10_1101-2020_10_26_351783	564	12	which	which	WDT
10_1101-2020_10_26_351783	564	13	the	the	DT
10_1101-2020_10_26_351783	564	14	module	module	NN
10_1101-2020_10_26_351783	564	15	is	be	VBZ
10_1101-2020_10_26_351783	564	16	significantly	significantly	RB
10_1101-2020_10_26_351783	564	17	enriched	enrich	VBN
10_1101-2020_10_26_351783	564	18	(	(	-LRB-
10_1101-2020_10_26_351783	564	19	red	red	JJ
10_1101-2020_10_26_351783	564	20	,	,	,
10_1101-2020_10_26_351783	564	21	alcohol	alcohol	NN
10_1101-2020_10_26_351783	564	22	use	use	NN
10_1101-2020_10_26_351783	564	23	;	;	:
10_1101-2020_10_26_351783	564	24	green	green	JJ
10_1101-2020_10_26_351783	564	25	,	,	,
10_1101-2020_10_26_351783	564	26	high	high	JJ
10_1101-2020_10_26_351783	564	27	BMI	bmi	NN
10_1101-2020_10_26_351783	564	28	;	;	:
10_1101-2020_10_26_351783	564	29	yellow	yellow	NN
10_1101-2020_10_26_351783	564	30	,	,	,
10_1101-2020_10_26_351783	564	31	smoking	smoking	NN
10_1101-2020_10_26_351783	564	32	;	;	:
10_1101-2020_10_26_351783	564	33	purple	purple	JJ
10_1101-2020_10_26_351783	564	34	,	,	,
10_1101-2020_10_26_351783	564	35	low	low	JJ
10_1101-2020_10_26_351783	564	36	sun	sun	NN
10_1101-2020_10_26_351783	564	37	exposure	exposure	NN
10_1101-2020_10_26_351783	564	38	;	;	:
10_1101-2020_10_26_351783	564	39	light	light	JJ
10_1101-2020_10_26_351783	564	40	blue	blue	NN
10_1101-2020_10_26_351783	564	41	,	,	,
10_1101-2020_10_26_351783	564	42	EBV	EBV	NNP
10_1101-2020_10_26_351783	564	43	infection	infection	NN
10_1101-2020_10_26_351783	564	44	;	;	:
10_1101-2020_10_26_351783	564	45	grey	grey	NNP
10_1101-2020_10_26_351783	564	46	,	,	,
10_1101-2020_10_26_351783	564	47	no	no	DT
10_1101-2020_10_26_351783	564	48	association	association	NN
10_1101-2020_10_26_351783	564	49	)	)	-RRB-
10_1101-2020_10_26_351783	564	50	.	.	.
10_1101-2020_10_26_351783	565	1	Edges	edge	NNS
10_1101-2020_10_26_351783	565	2	were	be	VBD
10_1101-2020_10_26_351783	565	3	extracted	extract	VBN
10_1101-2020_10_26_351783	565	4	from	from	IN
10_1101-2020_10_26_351783	565	5	the	the	DT
10_1101-2020_10_26_351783	565	6	STRINGdb	STRINGdb	NNP
10_1101-2020_10_26_351783	565	7	v11	v11	NN
10_1101-2020_10_26_351783	565	8	human	human	JJ
10_1101-2020_10_26_351783	565	9	PPI	PPI	NNP
10_1101-2020_10_26_351783	565	10	network	network	NN
10_1101-2020_10_26_351783	565	11	of	of	IN
10_1101-2020_10_26_351783	565	12	experimentally	experimentally	RB
10_1101-2020_10_26_351783	565	13	validated	validate	VBN
10_1101-2020_10_26_351783	565	14	interactions	interaction	NNS
10_1101-2020_10_26_351783	565	15	(	(	-LRB-
10_1101-2020_10_26_351783	565	16	confidence	confidence	NN
10_1101-2020_10_26_351783	565	17	score	score	NN
10_1101-2020_10_26_351783	565	18	>	>	XX
10_1101-2020_10_26_351783	565	19	700	700	CD
10_1101-2020_10_26_351783	565	20	)	)	-RRB-
10_1101-2020_10_26_351783	565	21	.	.	.
10_1101-2020_10_26_351783	566	1	SUPPLEMENTARY	supplementary	NN
10_1101-2020_10_26_351783	566	2	MATERIALS	material	NNS
10_1101-2020_10_26_351783	566	3	Supplementary	Supplementary	NNP
10_1101-2020_10_26_351783	566	4	Table	Table	NNP
10_1101-2020_10_26_351783	566	5	1	1	CD
10_1101-2020_10_26_351783	566	6	:	:	:
10_1101-2020_10_26_351783	566	7	All	all	DT
10_1101-2020_10_26_351783	566	8	case	case	NN
10_1101-2020_10_26_351783	566	9	-	-	HYPH
10_1101-2020_10_26_351783	566	10	control	control	NN
10_1101-2020_10_26_351783	566	11	comparisons	comparison	NNS
10_1101-2020_10_26_351783	566	12	used	use	VBN
10_1101-2020_10_26_351783	566	13	in	in	IN
10_1101-2020_10_26_351783	566	14	the	the	DT
10_1101-2020_10_26_351783	566	15	Transcriptomic	Transcriptomic	NNP
10_1101-2020_10_26_351783	566	16	and	and	CC
10_1101-2020_10_26_351783	566	17	Methylomic	methylomic	JJ
10_1101-2020_10_26_351783	566	18	benchmarks	benchmark	NNS
10_1101-2020_10_26_351783	566	19	.	.	.
10_1101-2020_10_26_351783	567	1	Supplementary	supplementary	JJ
10_1101-2020_10_26_351783	567	2	Table	table	NN
10_1101-2020_10_26_351783	567	3	2	2	CD
10_1101-2020_10_26_351783	567	4	:	:	:
10_1101-2020_10_26_351783	567	5	All	all	DT
10_1101-2020_10_26_351783	567	6	case	case	NN
10_1101-2020_10_26_351783	567	7	-	-	HYPH
10_1101-2020_10_26_351783	567	8	control	control	NN
10_1101-2020_10_26_351783	567	9	comparisons	comparison	NNS
10_1101-2020_10_26_351783	567	10	used	use	VBN
10_1101-2020_10_26_351783	567	11	in	in	IN
10_1101-2020_10_26_351783	567	12	the	the	DT
10_1101-2020_10_26_351783	567	13	MS	MS	NNP
10_1101-2020_10_26_351783	567	14	use	use	NN
10_1101-2020_10_26_351783	567	15	case	case	NN
10_1101-2020_10_26_351783	567	16	benchmark	benchmark	JJ
10_1101-2020_10_26_351783	567	17	.	.	.
10_1101-2020_10_26_351783	568	1	Supplementary	supplementary	JJ
10_1101-2020_10_26_351783	568	2	Table	table	NN
10_1101-2020_10_26_351783	568	3	3	3	CD
10_1101-2020_10_26_351783	568	4	:	:	:
10_1101-2020_10_26_351783	568	5	All	all	DT
10_1101-2020_10_26_351783	568	6	Methods	Methods	NNPS
10_1101-2020_10_26_351783	568	7	implemented	implement	VBN
10_1101-2020_10_26_351783	568	8	in	in	IN
10_1101-2020_10_26_351783	568	9	the	the	DT
10_1101-2020_10_26_351783	568	10	benchmark	benchmark	NN
10_1101-2020_10_26_351783	568	11	.	.	.
10_1101-2020_10_26_351783	569	1	(	(	-LRB-
10_1101-2020_10_26_351783	569	2	which	which	WDT
10_1101-2020_10_26_351783	569	3	was	be	VBD
10_1101-2020_10_26_351783	569	4	not	not	RB
10_1101-2020_10_26_351783	569	5	certified	certify	VBN
10_1101-2020_10_26_351783	569	6	by	by	IN
10_1101-2020_10_26_351783	569	7	peer	peer	NN
10_1101-2020_10_26_351783	569	8	review	review	NN
10_1101-2020_10_26_351783	569	9	)	)	-RRB-
10_1101-2020_10_26_351783	569	10	is	be	VBZ
10_1101-2020_10_26_351783	569	11	the	the	DT
10_1101-2020_10_26_351783	569	12	author	author	NN
10_1101-2020_10_26_351783	569	13	/	/	SYM
10_1101-2020_10_26_351783	569	14	funder	funder	NN
10_1101-2020_10_26_351783	569	15	.	.	.
10_1101-2020_10_26_351783	570	1	All	all	DT
10_1101-2020_10_26_351783	570	2	rights	right	NNS
10_1101-2020_10_26_351783	570	3	reserved	reserve	VBD
10_1101-2020_10_26_351783	570	4	.	.	.
10_1101-2020_10_26_351783	571	1	No	no	DT
10_1101-2020_10_26_351783	571	2	reuse	reuse	NN
10_1101-2020_10_26_351783	571	3	allowed	allow	VBN
10_1101-2020_10_26_351783	571	4	without	without	IN
10_1101-2020_10_26_351783	571	5	permission	permission	NN
10_1101-2020_10_26_351783	571	6	.	.	.
10_1101-2020_10_26_351783	572	1	The	the	DT
10_1101-2020_10_26_351783	572	2	copyright	copyright	NN
10_1101-2020_10_26_351783	572	3	holder	holder	NN
10_1101-2020_10_26_351783	572	4	for	for	IN
10_1101-2020_10_26_351783	572	5	this	this	DT
10_1101-2020_10_26_351783	572	6	preprintthis	preprintthis	NN
10_1101-2020_10_26_351783	572	7	version	version	NN
10_1101-2020_10_26_351783	572	8	posted	post	VBD
10_1101-2020_10_26_351783	572	9	January	January	NNP
10_1101-2020_10_26_351783	572	10	6	6	CD
10_1101-2020_10_26_351783	572	11	,	,	,
10_1101-2020_10_26_351783	572	12	2021	2021	CD
10_1101-2020_10_26_351783	572	13	.	.	.
10_1101-2020_10_26_351783	572	14	;	;	:
10_1101-2020_10_26_351783	572	15	https://doi.org/10.1101/2020.10.26.351783doi	https://doi.org/10.1101/2020.10.26.351783doi	ADD
10_1101-2020_10_26_351783	572	16	:	:	:
10_1101-2020_10_26_351783	572	17	bioRxiv	biorxiv	VB
10_1101-2020_10_26_351783	572	18	preprint	preprint	NN
10_1101-2020_10_26_351783	572	19	https://doi.org/10.1101/2020.10.26.351783	https://doi.org/10.1101/2020.10.26.351783	XX