Creating study carrel named bioinformatics-from-biorxiv
Initializing database
Creating cache from Bioarxiv xml file
10.1101/2021.01.08.425887
10_1101-2021_01_08_425887.pdf
/data-disk/reader-compute/reader-classic/bin/bioarxiv-harvest.sh: fork: retry: No child processes
10.1101/2021.01.08.425897
10_1101-2021_01_08_425897.pdf
10.1101/2021.01.08.425855
10_1101-2021_01_08_425855.pdf
/data-disk/reader-compute/reader-classic/bin/bioarxiv-harvest.sh: fork: retry: No child processes
10.1101/2020.03.27.012757
10_1101-2020_03_27_012757.pdf
10.1101/2021.01.08.425976
10_1101-2021_01_08_425976.pdf
/data-disk/reader-compute/reader-classic/bin/bioarxiv-harvest.sh: fork: retry: Resource temporarily unavailable
/data-disk/reader-compute/reader-classic/bin/bioarxiv-harvest.sh: fork: retry: No child processes
10.1101/2021.01.08.425918
10_1101-2021_01_08_425918.pdf
10.1101/2021.01.08.425952
10_1101-2021_01_08_425952.pdf
10.1101/2021.01.08.426008
10_1101-2021_01_08_426008.pdf
10.1101/2021.01.08.425967
10_1101-2021_01_08_425967.pdf
10.1101/2021.01.08.425379
10_1101-2021_01_08_425379.pdf
/data-disk/reader-compute/reader-classic/bin/bioarxiv-harvest.sh: fork: retry: Resource temporarily unavailable
10.1101/2020.04.17.043323
10_1101-2020_04_17_043323.pdf
/data-disk/reader-compute/reader-classic/bin/bioarxiv-harvest.sh: fork: retry: No child processes
10.1101/2021.01.06.425560
10_1101-2021_01_06_425560.pdf
10.1101/2021.01.06.425494
10_1101-2021_01_06_425494.pdf
10.1101/2021.01.07.425637
10_1101-2021_01_07_425637.pdf
/data-disk/reader-compute/reader-classic/bin/bioarxiv-harvest.sh: fork: retry: No child processes
/data-disk/reader-compute/reader-classic/bin/bioarxiv-harvest.sh: fork: retry: No child processes
10.1101/2021.01.06.425581
10_1101-2021_01_06_425581.pdf
10.1101/2021.01.07.425782
10_1101-2021_01_07_425782.pdf
10.1101/2021.01.07.425773
10_1101-2021_01_07_425773.pdf
10.1101/2021.01.07.425794
10_1101-2021_01_07_425794.pdf
10.1101/2021.01.07.425801
10_1101-2021_01_07_425801.pdf
10.1101/2021.01.08.425885
10_1101-2021_01_08_425885.pdf
10.1101/2020.10.26.351783
10_1101-2020_10_26_351783.pdf
10.1101/2021.01.06.425546
10_1101-2021_01_06_425546.pdf
/data-disk/reader-compute/reader-classic/bin/bioarxiv-harvest.sh: fork: retry: Resource temporarily unavailable
10.1101/2021.01.06.425544
10_1101-2021_01_06_425544.pdf
10.1101/2021.01.06.425569
10_1101-2021_01_06_425569.pdf
10.1101/2021.01.05.425266
10_1101-2021_01_05_425266.pdf
10.1101/2021.01.06.425550
10_1101-2021_01_06_425550.pdf
10.1101/2021.01.05.425414
10_1101-2021_01_05_425414.pdf
10.1101/2020.12.26.424429
10_1101-2020_12_26_424429.pdf
10.1101/2021.01.07.425716
10_1101-2021_01_07_425716.pdf
10.1101/2021.01.07.425697
10_1101-2021_01_07_425697.pdf
10.1101/2020.11.13.381475
10_1101-2020_11_13_381475.pdf
10.1101/2021.01.04.425315
10_1101-2021_01_04_425315.pdf
10.1101/2021.01.05.425384
10_1101-2021_01_05_425384.pdf
10.1101/2020.05.22.110247
10_1101-2020_05_22_110247.pdf
10.1101/2021.01.02.425006
10_1101-2021_01_02_425006.pdf
10.1101/332965
10_1101-332965.pdf
10.1101/2021.01.05.425409
10_1101-2021_01_05_425409.pdf
10.1101/2021.01.05.425417
10_1101-2021_01_05_425417.pdf
10.1101/2021.01.05.425508
10_1101-2021_01_05_425508.pdf
/data-disk/reader-compute/reader-classic/bin/bioarxiv-harvest.sh: fork: retry: No child processes
10.1101/2021.01.04.425335
10_1101-2021_01_04_425335.pdf
10.1101/2020.12.14.422697
10_1101-2020_12_14_422697.pdf
10.1101/2020.01.29.925354
10_1101-2020_01_29_925354.pdf
10.1101/2020.08.13.249839
10_1101-2020_08_13_249839.pdf
/data-disk/reader-compute/reader-classic/bin/bioarxiv-harvest.sh: fork: retry: No child processes
10.1101/2021.01.04.425250
10_1101-2021_01_04_425250.pdf
10.1101/2020.09.09.289074
10_1101-2020_09_09_289074.pdf
10.1101/2021.01.04.425285
10_1101-2021_01_04_425285.pdf
10.1101/2020.12.24.424332
10_1101-2020_12_24_424332.pdf
10.1101/2021.01.04.425288
10_1101-2021_01_04_425288.pdf
10.1101/436634
10_1101-436634.pdf
10.1101/2020.08.28.271981
10_1101-2020_08_28_271981.pdf
parallel: Warning: Only enough available processes to run 50 jobs in parallel.
parallel: Warning: Raising ulimit -u or /etc/security/limits.conf
parallel: Warning: or /proc/sys/kernel/pid_max may help.
2021-01-10 13:53:39 URL:https://www.biorxiv.org/ [37867/37867] -> "./cache/10_1101-2021_01_08_425976.pdf" [1]
2021-01-10 13:53:39 URL:https://www.biorxiv.org/ [37867/37867] -> "./cache/10_1101-2021_01_06_425494.pdf" [1]
parallel: Warning: No more processes: Decreasing number of running jobs to 49.
parallel: Warning: Raising ulimit -u or /etc/security/limits.conf may help.
2021-01-10 13:53:39 URL:https://www.biorxiv.org/ [37867/37867] -> "./cache/10_1101-2021_01_06_425546.pdf" [1]
2021-01-10 13:53:40 URL:https://www.biorxiv.org/content/10.1101/2021.01.08.425855v1.full.pdf [997276] -> "./cache/10_1101-2021_01_08_425855.pdf" [1]
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2021-01-10 13:53:40 URL:https://www.biorxiv.org/content/10.1101/2020.10.26.351783v2.full.pdf [464291] -> "./cache/10_1101-2020_10_26_351783.pdf" [1]
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=== updating bibliographic database
/data-disk/reader-compute/reader-classic/bin/bioarxiv2cache.sh: fork: retry: No child processes
Building study carrel named bioinformatics-from-biorxiv
parallel: Warning: Only enough available processes to run 21 jobs in parallel.
parallel: Warning: Raising ulimit -u or /etc/security/limits.conf
parallel: Warning: or /proc/sys/kernel/pid_max may help.
parallel: Warning: No more processes: Decreasing number of running jobs to 20.
parallel: Warning: Raising ulimit -u or /etc/security/limits.conf may help.
FILE: cache/10_1101-2021_01_08_425976.pdf
OUTPUT: txt/10_1101-2021_01_08_425976.txt
FILE: cache/10_1101-2021_01_08_425887.pdf
OUTPUT: txt/10_1101-2021_01_08_425887.txt
FILE: cache/10_1101-2021_01_08_425897.pdf
OUTPUT: txt/10_1101-2021_01_08_425897.txt
FILE: cache/10_1101-2021_01_08_426008.pdf
OUTPUT: txt/10_1101-2021_01_08_426008.txt
FILE: cache/10_1101-2021_01_08_425967.pdf
OUTPUT: txt/10_1101-2021_01_08_425967.txt
FILE: cache/10_1101-2021_01_06_425581.pdf
OUTPUT: txt/10_1101-2021_01_06_425581.txt
FILE: cache/10_1101-2021_01_07_425801.pdf
OUTPUT: txt/10_1101-2021_01_07_425801.txt
FILE: cache/10_1101-2021_01_06_425560.pdf
OUTPUT: txt/10_1101-2021_01_06_425560.txt
FILE: cache/10_1101-2020_04_17_043323.pdf
OUTPUT: txt/10_1101-2020_04_17_043323.txt
FILE: cache/10_1101-2021_01_06_425494.pdf
OUTPUT: txt/10_1101-2021_01_06_425494.txt
FILE: cache/10_1101-2021_01_07_425782.pdf
OUTPUT: txt/10_1101-2021_01_07_425782.txt
FILE: cache/10_1101-2021_01_07_425637.pdf
OUTPUT: txt/10_1101-2021_01_07_425637.txt
FILE: cache/10_1101-2020_03_27_012757.pdf
OUTPUT: txt/10_1101-2020_03_27_012757.txt
FILE: cache/10_1101-2021_01_08_425952.pdf
OUTPUT: txt/10_1101-2021_01_08_425952.txt
FILE: cache/10_1101-2021_01_07_425794.pdf
OUTPUT: txt/10_1101-2021_01_07_425794.txt
FILE: cache/10_1101-2021_01_07_425773.pdf
OUTPUT: txt/10_1101-2021_01_07_425773.txt
FILE: cache/10_1101-2021_01_08_425918.pdf
OUTPUT: txt/10_1101-2021_01_08_425918.txt
FILE: cache/10_1101-2021_01_06_425546.pdf
OUTPUT: txt/10_1101-2021_01_06_425546.txt
/data-disk/reader-compute/reader-classic/bin/file2txt.sh: fork: retry: No child processes
FILE: cache/10_1101-2021_01_08_425855.pdf
OUTPUT: txt/10_1101-2021_01_08_425855.txt
FILE: cache/10_1101-2021_01_08_425885.pdf
OUTPUT: txt/10_1101-2021_01_08_425885.txt
FILE: cache/10_1101-2021_01_06_425544.pdf
OUTPUT: txt/10_1101-2021_01_06_425544.txt
FILE: cache/10_1101-2020_10_26_351783.pdf
OUTPUT: txt/10_1101-2020_10_26_351783.txt
FILE: cache/10_1101-2021_01_07_425697.pdf
OUTPUT: txt/10_1101-2021_01_07_425697.txt
FILE: cache/10_1101-2020_12_26_424429.pdf
OUTPUT: txt/10_1101-2020_12_26_424429.txt
FILE: cache/10_1101-2021_01_05_425266.pdf
OUTPUT: txt/10_1101-2021_01_05_425266.txt
FILE: cache/10_1101-2021_01_05_425414.pdf
OUTPUT: txt/10_1101-2021_01_05_425414.txt
FILE: cache/10_1101-2021_01_06_425569.pdf
OUTPUT: txt/10_1101-2021_01_06_425569.txt
FILE: cache/10_1101-2021_01_05_425384.pdf
OUTPUT: txt/10_1101-2021_01_05_425384.txt
FILE: cache/10_1101-2021_01_07_425716.pdf
OUTPUT: txt/10_1101-2021_01_07_425716.txt
FILE: cache/10_1101-332965.pdf
OUTPUT: txt/10_1101-332965.txt
FILE: cache/10_1101-2021_01_05_425409.pdf
OUTPUT: txt/10_1101-2021_01_05_425409.txt
FILE: cache/10_1101-2020_11_13_381475.pdf
OUTPUT: txt/10_1101-2020_11_13_381475.txt
FILE: cache/10_1101-2021_01_02_425006.pdf
OUTPUT: txt/10_1101-2021_01_02_425006.txt
FILE: cache/10_1101-2021_01_04_425315.pdf
OUTPUT: txt/10_1101-2021_01_04_425315.txt
FILE: cache/10_1101-2020_05_22_110247.pdf
OUTPUT: txt/10_1101-2020_05_22_110247.txt
FILE: cache/10_1101-2020_12_14_422697.pdf
OUTPUT: txt/10_1101-2020_12_14_422697.txt
FILE: cache/10_1101-2021_01_06_425550.pdf
OUTPUT: txt/10_1101-2021_01_06_425550.txt
FILE: cache/10_1101-2020_12_24_424332.pdf
OUTPUT: txt/10_1101-2020_12_24_424332.txt
/data-disk/reader-compute/reader-classic/bin/file2txt.sh: fork: retry: No child processes
FILE: cache/10_1101-2021_01_05_425508.pdf
OUTPUT: txt/10_1101-2021_01_05_425508.txt
FILE: cache/10_1101-2020_09_09_289074.pdf
OUTPUT: txt/10_1101-2020_09_09_289074.txt
FILE: cache/10_1101-2021_01_04_425288.pdf
OUTPUT: txt/10_1101-2021_01_04_425288.txt
FILE: cache/10_1101-2020_01_29_925354.pdf
OUTPUT: txt/10_1101-2020_01_29_925354.txt
FILE: cache/10_1101-2021_01_04_425250.pdf
OUTPUT: txt/10_1101-2021_01_04_425250.txt
FILE: cache/10_1101-2021_01_05_425417.pdf
OUTPUT: txt/10_1101-2021_01_05_425417.txt
/data-disk/reader-compute/reader-classic/bin/file2txt.sh: fork: retry: No child processes
FILE: cache/10_1101-2021_01_04_425285.pdf
OUTPUT: txt/10_1101-2021_01_04_425285.txt
FILE: cache/10_1101-436634.pdf
OUTPUT: txt/10_1101-436634.txt
FILE: cache/10_1101-2020_08_13_249839.pdf
OUTPUT: txt/10_1101-2020_08_13_249839.txt
FILE: cache/10_1101-2020_08_28_271981.pdf
OUTPUT: txt/10_1101-2020_08_28_271981.txt
FILE: cache/10_1101-2021_01_04_425335.pdf
OUTPUT: txt/10_1101-2021_01_04_425335.txt
FILE: cache/10_1101-2021_01_08_425379.pdf
OUTPUT: txt/10_1101-2021_01_08_425379.txt
parallel: Warning: Cannot spawn any jobs. Raising ulimit -u or 'nproc' in /etc/security/limits.conf
parallel: Warning: or /proc/sys/kernel/pid_max may help.
parallel: Warning: Only enough available processes to run 9 jobs in parallel.
parallel: Warning: Raising ulimit -u or /etc/security/limits.conf
parallel: Warning: or /proc/sys/kernel/pid_max may help.
parallel: Warning: Only enough available processes to run 1 jobs in parallel.
parallel: Warning: Raising ulimit -u or /etc/security/limits.conf
parallel: Warning: or /proc/sys/kernel/pid_max may help.
parallel: Warning: Only enough available processes to run 15 jobs in parallel.
parallel: Warning: Raising ulimit -u or /etc/security/limits.conf
parallel: Warning: or /proc/sys/kernel/pid_max may help.
parallel: Warning: No more processes: Decreasing number of running jobs to 14.
parallel: Warning: Raising ulimit -u or /etc/security/limits.conf may help.
parallel: Warning: Only enough available processes to run 29 jobs in parallel.
parallel: Warning: Raising ulimit -u or /etc/security/limits.conf
parallel: Warning: or /proc/sys/kernel/pid_max may help.
/data-disk/reader-compute/reader-classic/bin/txt2urls.sh: fork: retry: Resource temporarily unavailable
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10_1101-2021_01_08_425976 txt/../ent/10_1101-2021_01_08_425976.ent
10_1101-2021_01_06_425494 txt/../pos/10_1101-2021_01_06_425494.pos
10_1101-2021_01_08_425976 txt/../pos/10_1101-2021_01_08_425976.pos
10_1101-2021_01_06_425494 txt/../ent/10_1101-2021_01_06_425494.ent
10_1101-2021_01_06_425546 txt/../ent/10_1101-2021_01_06_425546.ent
parallel: Warning: No more processes: Decreasing number of running jobs to 49.
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/data-disk/reader-compute/reader-classic/bin/txt2keywords.sh: fork: retry: No child processes
10_1101-2021_01_08_425976 txt/../wrd/10_1101-2021_01_08_425976.wrd
10_1101-2021_01_07_425782 txt/../ent/10_1101-2021_01_07_425782.ent
10_1101-2021_01_08_425967 txt/../pos/10_1101-2021_01_08_425967.pos
10_1101-2021_01_08_426008 txt/../pos/10_1101-2021_01_08_426008.pos
10_1101-2021_01_08_425967 txt/../wrd/10_1101-2021_01_08_425967.wrd
parallel: Warning: No more processes: Decreasing number of running jobs to 8.
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/data-disk/reader-compute/reader-classic/bin/txt2keywords.sh: fork: retry: No child processes
10_1101-2021_01_08_426008 txt/../wrd/10_1101-2021_01_08_426008.wrd
=== file2bib.sh ===
OMP: Error #34: System unable to allocate necessary resources for OMP thread:
OMP: System error #11: Resource temporarily unavailable
OMP: Hint Try decreasing the value of OMP_NUM_THREADS.
/data-disk/reader-compute/reader-classic/bin/file2bib.sh: line 39: 37738 Aborted $FILE2BIB "$FILE" > "$OUTPUT"
10_1101-2021_01_08_425967 txt/../ent/10_1101-2021_01_08_425967.ent
=== file2bib.sh ===
OMP: Error #34: System unable to allocate necessary resources for OMP thread:
OMP: System error #11: Resource temporarily unavailable
OMP: Hint Try decreasing the value of OMP_NUM_THREADS.
/data-disk/reader-compute/reader-classic/bin/file2bib.sh: line 39: 37879 Aborted $FILE2BIB "$FILE" > "$OUTPUT"
=== file2bib.sh ===
OMP: Error #34: System unable to allocate necessary resources for OMP thread:
OMP: System error #11: Resource temporarily unavailable
OMP: Hint Try decreasing the value of OMP_NUM_THREADS.
/data-disk/reader-compute/reader-classic/bin/file2bib.sh: line 39: 38133 Aborted $FILE2BIB "$FILE" > "$OUTPUT"
10_1101-2021_01_08_426008 txt/../ent/10_1101-2021_01_08_426008.ent
10_1101-2021_01_08_425887 txt/../pos/10_1101-2021_01_08_425887.pos
10_1101-2020_12_26_424429 txt/../ent/10_1101-2020_12_26_424429.ent
=== file2bib.sh ===
OMP: Error #34: System unable to allocate necessary resources for OMP thread:
OMP: System error #11: Resource temporarily unavailable
OMP: Hint Try decreasing the value of OMP_NUM_THREADS.
/data-disk/reader-compute/reader-classic/bin/file2bib.sh: line 39: 38093 Aborted $FILE2BIB "$FILE" > "$OUTPUT"
=== file2bib.sh ===
OMP: Error #34: System unable to allocate necessary resources for OMP thread:
OMP: System error #11: Resource temporarily unavailable
OMP: Hint Try decreasing the value of OMP_NUM_THREADS.
/data-disk/reader-compute/reader-classic/bin/file2bib.sh: line 39: 37790 Aborted $FILE2BIB "$FILE" > "$OUTPUT"
=== file2bib.sh ===
OMP: Error #34: System unable to allocate necessary resources for OMP thread:
OMP: System error #11: Resource temporarily unavailable
OMP: Hint Try decreasing the value of OMP_NUM_THREADS.
/data-disk/reader-compute/reader-classic/bin/file2bib.sh: line 39: 37921 Aborted $FILE2BIB "$FILE" > "$OUTPUT"
/data-disk/reader-compute/reader-classic/bin/txt2urls.sh: fork: retry: No child processes
/data-disk/reader-compute/reader-classic/bin/txt2urls.sh: fork: retry: No child processes
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10_1101-2020_04_17_043323 txt/../wrd/10_1101-2020_04_17_043323.wrd
=== file2bib.sh ===
OMP: Error #34: System unable to allocate necessary resources for OMP thread:
OMP: System error #11: Resource temporarily unavailable
OMP: Hint Try decreasing the value of OMP_NUM_THREADS.
/data-disk/reader-compute/reader-classic/bin/file2bib.sh: line 39: 37739 Aborted $FILE2BIB "$FILE" > "$OUTPUT"
10_1101-2021_01_06_425581 txt/../pos/10_1101-2021_01_06_425581.pos
=== file2bib.sh ===
OMP: Error #34: System unable to allocate necessary resources for OMP thread:
OMP: System error #11: Resource temporarily unavailable
OMP: Hint Try decreasing the value of OMP_NUM_THREADS.
/data-disk/reader-compute/reader-classic/bin/file2bib.sh: line 39: 37628 Aborted $FILE2BIB "$FILE" > "$OUTPUT"
10_1101-2021_01_08_425887 txt/../wrd/10_1101-2021_01_08_425887.wrd
10_1101-2021_01_08_425918 txt/../wrd/10_1101-2021_01_08_425918.wrd
10_1101-2020_04_17_043323 txt/../pos/10_1101-2020_04_17_043323.pos
10_1101-2020_04_17_043323 txt/../ent/10_1101-2020_04_17_043323.ent
10_1101-2021_01_07_425773 txt/../ent/10_1101-2021_01_07_425773.ent
=== file2bib.sh ===
/data-disk/reader-compute/reader-classic/bin/file2bib.sh: fork: retry: No child processes
/data-disk/reader-compute/reader-classic/bin/file2bib.sh: fork: retry: No child processes
OMP: Error #34: System unable to allocate necessary resources for OMP thread:
OMP: System error #11: Resource temporarily unavailable
OMP: Hint Try decreasing the value of OMP_NUM_THREADS.
/data-disk/reader-compute/reader-classic/bin/file2bib.sh: line 39: 39125 Aborted $FILE2BIB "$FILE" > "$OUTPUT"
=== file2bib.sh ===
OMP: Error #34: System unable to allocate necessary resources for OMP thread:
OMP: System error #11: Resource temporarily unavailable
OMP: Hint Try decreasing the value of OMP_NUM_THREADS.
/data-disk/reader-compute/reader-classic/bin/file2bib.sh: line 39: 38082 Aborted $FILE2BIB "$FILE" > "$OUTPUT"
10_1101-2021_01_08_425887 txt/../ent/10_1101-2021_01_08_425887.ent
/data-disk/reader-compute/reader-classic/bin/txt2pos.sh: fork: retry: No child processes
/data-disk/reader-compute/reader-classic/bin/txt2pos.sh: fork: retry: No child processes
10_1101-2021_01_08_425918 txt/../pos/10_1101-2021_01_08_425918.pos
=== file2bib.sh ===
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10_1101-2021_01_06_425550 txt/../ent/10_1101-2021_01_06_425550.ent
=== file2bib.sh ===
OMP: Error #34: System unable to allocate necessary resources for OMP thread:
OMP: System error #11: Resource temporarily unavailable
OMP: Hint Try decreasing the value of OMP_NUM_THREADS.
/data-disk/reader-compute/reader-classic/bin/file2bib.sh: line 39: 37978 Aborted $FILE2BIB "$FILE" > "$OUTPUT"
=== file2bib.sh ===
OMP: Error #34: System unable to allocate necessary resources for OMP thread:
OMP: System error #11: Resource temporarily unavailable
OMP: Hint Try decreasing the value of OMP_NUM_THREADS.
/data-disk/reader-compute/reader-classic/bin/file2bib.sh: line 39: 37922 Aborted $FILE2BIB "$FILE" > "$OUTPUT"
=== file2bib.sh ===
id: 10_1101-2021_01_08_425976
author: Ahuja, Yuri
title: Semi-supervised Calibration of Risk with Noisy Event Times (SCORNET) Using Electronic Health Record Data
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10_1101-2021_01_08_425952 txt/../pos/10_1101-2021_01_08_425952.pos
=== file2bib.sh ===
OMP: Error #34: System unable to allocate necessary resources for OMP thread:
OMP: System error #11: Resource temporarily unavailable
OMP: Hint Try decreasing the value of OMP_NUM_THREADS.
/data-disk/reader-compute/reader-classic/bin/file2bib.sh: line 39: 37448 Aborted $FILE2BIB "$FILE" > "$OUTPUT"
=== file2bib.sh ===
OMP: Error #34: System unable to allocate necessary resources for OMP thread:
OMP: System error #11: Resource temporarily unavailable
OMP: Hint Try decreasing the value of OMP_NUM_THREADS.
/data-disk/reader-compute/reader-classic/bin/file2bib.sh: line 39: 38129 Aborted $FILE2BIB "$FILE" > "$OUTPUT"
10_1101-2021_01_08_425897 txt/../wrd/10_1101-2021_01_08_425897.wrd
=== file2bib.sh ===
OMP: Error #34: System unable to allocate necessary resources for OMP thread:
OMP: System error #11: Resource temporarily unavailable
OMP: Hint Try decreasing the value of OMP_NUM_THREADS.
/data-disk/reader-compute/reader-classic/bin/file2bib.sh: line 39: 38086 Aborted $FILE2BIB "$FILE" > "$OUTPUT"
=== file2bib.sh ===
/data-disk/reader-compute/reader-classic/bin/file2bib.sh: fork: retry: No child processes
id: 10_1101-2021_01_06_425546
author: Osthus, Dave
title: Fast and Accurate Influenza Forecasting in the United States with Inferno
date: 2021
pages:
extension: .pdf
txt: ./txt/10_1101-2021_01_06_425546.txt
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Content-Encoding UTF-8
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viewport width=device-width, initial-scale=1, maximum-scale=3, minimum-scale=1, user-scalable=yes
=== file2bib.sh ===
OMP: Error #34: System unable to allocate necessary resources for OMP thread:
OMP: System error #11: Resource temporarily unavailable
OMP: Hint Try decreasing the value of OMP_NUM_THREADS.
/data-disk/reader-compute/reader-classic/bin/file2bib.sh: line 39: 38048 Aborted $FILE2BIB "$FILE" > "$OUTPUT"
10_1101-2021_01_08_425952 txt/../wrd/10_1101-2021_01_08_425952.wrd
=== file2bib.sh ===
OMP: Error #34: System unable to allocate necessary resources for OMP thread:
OMP: System error #11: Resource temporarily unavailable
OMP: Hint Try decreasing the value of OMP_NUM_THREADS.
/data-disk/reader-compute/reader-classic/bin/file2bib.sh: line 39: 37986 Aborted $FILE2BIB "$FILE" > "$OUTPUT"
=== file2bib.sh ===
OMP: Error #34: System unable to allocate necessary resources for OMP thread:
OMP: System error #11: Resource temporarily unavailable
OMP: Hint Try decreasing the value of OMP_NUM_THREADS.
/data-disk/reader-compute/reader-classic/bin/file2bib.sh: line 39: 38036 Aborted $FILE2BIB "$FILE" > "$OUTPUT"
=== file2bib.sh ===
OMP: Error #34: System unable to allocate necessary resources for OMP thread:
OMP: System error #11: Resource temporarily unavailable
OMP: Hint Try decreasing the value of OMP_NUM_THREADS.
/data-disk/reader-compute/reader-classic/bin/file2bib.sh: line 39: 38124 Aborted $FILE2BIB "$FILE" > "$OUTPUT"
10_1101-2021_01_08_425952 txt/../ent/10_1101-2021_01_08_425952.ent
=== file2bib.sh ===
/data-disk/reader-compute/reader-classic/bin/file2bib.sh: fork: retry: No child processes
OMP: Error #34: System unable to allocate necessary resources for OMP thread:
OMP: System error #11: Resource temporarily unavailable
OMP: Hint Try decreasing the value of OMP_NUM_THREADS.
/data-disk/reader-compute/reader-classic/bin/file2bib.sh: line 39: 38252 Aborted $FILE2BIB "$FILE" > "$OUTPUT"
10_1101-2021_01_08_425855 txt/../ent/10_1101-2021_01_08_425855.ent
10_1101-2021_01_07_425637 txt/../pos/10_1101-2021_01_07_425637.pos
10_1101-2021_01_08_425897 txt/../pos/10_1101-2021_01_08_425897.pos
10_1101-2021_01_06_425581 txt/../ent/10_1101-2021_01_06_425581.ent
10_1101-2021_01_07_425782 txt/../pos/10_1101-2021_01_07_425782.pos
10_1101-2021_01_05_425384 txt/../ent/10_1101-2021_01_05_425384.ent
=== file2bib.sh ===
OMP: Error #34: System unable to allocate necessary resources for OMP thread:
OMP: System error #11: Resource temporarily unavailable
OMP: Hint Try decreasing the value of OMP_NUM_THREADS.
/data-disk/reader-compute/reader-classic/bin/file2bib.sh: line 39: 38134 Aborted $FILE2BIB "$FILE" > "$OUTPUT"
=== file2bib.sh ===
OMP: Error #34: System unable to allocate necessary resources for OMP thread:
OMP: System error #11: Resource temporarily unavailable
OMP: Hint Try decreasing the value of OMP_NUM_THREADS.
/data-disk/reader-compute/reader-classic/bin/file2bib.sh: line 39: 37994 Aborted $FILE2BIB "$FILE" > "$OUTPUT"
10_1101-2021_01_06_425544 txt/../ent/10_1101-2021_01_06_425544.ent
10_1101-2021_01_07_425801 txt/../pos/10_1101-2021_01_07_425801.pos
10_1101-2021_01_06_425494 txt/../wrd/10_1101-2021_01_06_425494.wrd
parallel: Warning: No more processes: Decreasing number of running jobs to 13.
parallel: Warning: Raising ulimit -u or /etc/security/limits.conf may help.
10_1101-2021_01_08_425918 txt/../ent/10_1101-2021_01_08_425918.ent
10_1101-2021_01_07_425697 txt/../ent/10_1101-2021_01_07_425697.ent
10_1101-2021_01_07_425794 txt/../ent/10_1101-2021_01_07_425794.ent
10_1101-2021_01_07_425794 txt/../pos/10_1101-2021_01_07_425794.pos
=== file2bib.sh ===
OMP: Error #34: System unable to allocate necessary resources for OMP thread:
OMP: System error #11: Resource temporarily unavailable
OMP: Hint Try decreasing the value of OMP_NUM_THREADS.
/data-disk/reader-compute/reader-classic/bin/file2bib.sh: line 39: 38099 Aborted $FILE2BIB "$FILE" > "$OUTPUT"
10_1101-2021_01_07_425801 txt/../ent/10_1101-2021_01_07_425801.ent
=== file2bib.sh ===
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10_1101-2021_01_05_425266 txt/../ent/10_1101-2021_01_05_425266.ent
/data-disk/reader-compute/reader-classic/bin/txt2pos.sh: fork: retry: No child processes
10_1101-2021_01_06_425546 txt/../pos/10_1101-2021_01_06_425546.pos
10_1101-2021_01_07_425773 txt/../pos/10_1101-2021_01_07_425773.pos
=== file2bib.sh ===
OMP: Error #34: System unable to allocate necessary resources for OMP thread:
OMP: System error #11: Resource temporarily unavailable
OMP: Hint Try decreasing the value of OMP_NUM_THREADS.
/data-disk/reader-compute/reader-classic/bin/file2bib.sh: line 39: 38094 Aborted $FILE2BIB "$FILE" > "$OUTPUT"
10_1101-2021_01_06_425560 txt/../pos/10_1101-2021_01_06_425560.pos
=== file2bib.sh ===
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Done mapping.
Reducing bioinformatics-from-biorxiv
=== reduce.pl bib ===
id = 10_1101-2021_01_08_425887
author = Hu, Yan
title = Auto-CORPus: Automated and Consistent Outputs from Research Publications
date = 2021
pages = 10
extension = .pdf
mime = application/pdf
words = 6886
sentences = 553
flesch = 53
summary = the same structured model, so that these can be used as input to rule-based or deep learning algorithms for data extraction. example, at this point in this article the main headers are 'abstract' followed by 'introduction' and 'materials and methods' that could make up a digraph. We use this process to evaluate new potential synonyms for existing terms and identify abstract → introduction → materials → results → discussion → conclusion → acknowledgements → footnotes section → references. Based on the digraph, we then assigned data and data description to be synonyms of the materials section, and participants From the analysis of ego-networks four new potential categories were identified: disclosure, graphical abstract, highlights and participants. Newly identified synonyms for existing IAO terms (00006xx) from the digraph mapping of 2,441 publications. Newly identified synonyms for existing IAO terms (00006xx) from the digraph mapping of 2,441 publications.
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id = 10_1101-2021_01_08_425855
author = Wu, Canbiao
title = DeepHBV: A deep learning model to predict hepatitis B virus (HBV) integration sites.
date = 2021
pages = 31
extension = .pdf
mime = application/pdf
words = 7280
sentences = 806
flesch = 53
summary = DeepHBV: A deep learning model to predict hepatitis B virus (HBV) integration sites. deep learning model DeepHBV to predict HBV integration sites by learning local learning model DeepHBV to predict HBV integration sites by learning local genomic DeepHBV effectively predicts HBV integration sites by adding genomic features. mixed HBV integration sequences, positive genome feature samples, and randomly peaks and DeepHBV with HBV integration sequences + TCGA Pan Cancer peaks) on model trained with HBV integrated sequences + TCGA Pan Cancer showed an performed better compared with DeepHBV model with HBV integration sequences + HBV integration sites + TCGA Pan Cancer, a cluster of attention weights much output of DeepHBV with HBV integration sites plus TCGA Pan Cancer showed the of DeepHBV with HBV integration sequences + TCGA Pan Cancer showed strong DeepHBV with HBV integration sequences + TCGA Pan Cancer model on (a) DeepHBV with HBV integration sequences + TCGA Pan Cancer model on (a)
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id = 10_1101-2021_01_08_425976
author = Ahuja, Yuri
title = Semi-supervised Calibration of Risk with Noisy Event Times (SCORNET) Using Electronic Health Record Data
date = 2021
pages =
extension = .pdf
mime = application/xhtml+xml
words = 72
sentences = 16
flesch = 15
summary = bioRxiv.org the preprint server for Biology Skip to main content Home Submit ALERTS / RSS Search for this keyword Advanced Search Subject Areas All Articles Animal Behavior and Cognition Biochemistry Bioengineering Bioinformatics Biophysics Cancer Biology Cell Biology Clinical Trials Developmental Biology Ecology Epidemiology Evolutionary Biology Genetics Genomics Immunology Microbiology Molecular Biology Neuroscience Paleontology Pathology Pharmacology and Toxicology Physiology Plant Biology Scientific Communication and Education Synthetic Biology Systems Biology Zoology View by Month
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id = 10_1101-2021_01_08_426008
author = Pipes, Lenore
title = AncestralClust: Clustering of Divergent Nucleotide Sequences by Ancestral Sequence Reconstruction using Phylogenetic Trees
date = 2021
pages = 7
extension = .pdf
mime = application/pdf
words = 3682
sentences = 382
flesch = 63
summary = AncestralClust: Clustering of Divergent Nucleotide Sequences by Ancestral Sequence Reconstruction using Phylogenetic Trees Despite the exponential increase in the size of sequence databases of homologous genes, few methods exist to cluster At low identities, these methods produce uneven clusters where the majority of sequences are are no clustering methods that can accurately cluster large taxonomically divergent metabarcoding reference databases such as databases (Schoch et al., 2020), there is a need for new computationally efficient methods that can cluster divergent sequences. To cluster divergent sequences, we developed AncestralClust clustering methods: UCLUST (Edgar, 2010), meshclust2 (James dataset against UCLUST because it is the most widely used clustering program and it performs better than CD-HIT on low identity We developed a phylogenetic-based clustering method, AncestralClust, specifically to cluster divergent metabarcode sequences. Comparisons of clustering methods using 13,043 COI sequences from 11 different species.
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id = 10_1101-2020_04_17_043323
author = Waschke, Johannes
title = linus: Conveniently explore, share, and present large-scale biological trajectory data from a web browser
date = 2021
pages = 13
extension = .pdf
mime = application/pdf
words = 6528
sentences = 571
flesch = 54
summary = linus: Conveniently explore, share, and present large-scale biological trajectory data from a web browser linus: Conveniently explore, share, and present large-scale biological trajectory data In biology, we are often confronted with information-rich, large-scale trajectory data, but exploring and communicating We provide a python script that reads trajectory data and enriches them with additional features, such as edge bundling or custom axes and generates an interactive web-based visualisation that can be shared offline from diffusion MRI imaging (Liu et al., 2020), or tracking data such as cell trajectories or animal trails (Romero-Ferrero et visualisation tool linus, making it easier to explore 3D trajectory data from any device without a local installation of Creating a visualisation package with linus is done in a few simple steps (Fig. 1a): The user imports trajectory data from a Figure 1 Browser-based exploration and sharing of trajectory visualizations with linus.
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id = 10_1101-2021_01_06_425560
author = Lengyel, Attila
title = Review and performance evaluation of trait-based between-community dissimilarity measures
date = 2021
pages = 50
extension = .pdf
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words = 15548
sentences = 2320
flesch = 67
summary = Review and performance evaluation of trait-based between-community dissimilarity measures 2 Review and performance evaluation of trait-based between-community dissimilarity measures 2 2. In this paper we reviewed the trait-based dissimilarity indices available in the 16 dissimilarities calculated by different indices correlate with environmental distances. beta diversity, dissimilarity index, distance metric, community ecology, functional traits 39 including several families of trait-based dissimilarity indices. FDissim indices incorporate trait information into the calculation of dissimilarity in different 162 Indices following this approach represent each community with a typical trait value, and 185 2005) or trait-based dissimilarity of species (Lepš 220 of the similarity indices for presence/absence data disregarding species properties, while the 281 ordinariness values in the species-based (dis-)similarity indices. Ricotta & Pavoine (2015) introduced a new family of trait-based similarity measures called 331 For species-based analyses, Ricotta & Podani (2017) suggested a general formula of distance 336 compared how strongly the dissimilarity indices correlate with the environmental distance 515
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id = 10_1101-2021_01_06_425494
author = Baldwin, Quenisha
title = The topological free energy of proteins
date = 2021
pages =
extension = .pdf
mime = application/xhtml+xml
words = 72
sentences = 16
flesch = 15
summary = bioRxiv.org the preprint server for Biology Skip to main content Home Submit ALERTS / RSS Search for this keyword Advanced Search Subject Areas All Articles Animal Behavior and Cognition Biochemistry Bioengineering Bioinformatics Biophysics Cancer Biology Cell Biology Clinical Trials Developmental Biology Ecology Epidemiology Evolutionary Biology Genetics Genomics Immunology Microbiology Molecular Biology Neuroscience Paleontology Pathology Pharmacology and Toxicology Physiology Plant Biology Scientific Communication and Education Synthetic Biology Systems Biology Zoology View by Month
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id = 10_1101-2021_01_07_425637
author = Arneson, Adriana
title = A mammalian methylation array for profiling methylation levels at conserved sequences
date = 2021
pages = 39
extension = .pdf
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words = 12153
sentences = 1117
flesch = 58
summary = characterize the CpGs on the mammalian methylation array with various genomic annotations. Array probes are sequences of length 50bp flanking a target CpG based on the human reference We added probes targeting 1986 CpGs to the mammalian methylation array based on All 37488 CpGs profiled on the mammalian methylation array apply to humans, but only a CpGs on the mammalian array cover 6871 human and 5659 mouse genes when each DNA methylation samples for three species: human (n=10 arrays), mouse (n=20), and rat (n=15), synthetic DNA data from 3 species: human (n=10 mammalian arrays), mouse (n=20), and rat CpG and gene coverage of probes on the mammalian methylation array across CpG island and chromatin state analysis of mammalian methylation probes. probes targeting the same CpG that can also be found on the human EPIC array that were not mammalian methylation array to the human (hg19) and mouse (mm10) genome using QUASR
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id = 10_1101-2021_01_07_425801
author = Hou, Yapeng
title = Fibrinolysis influences SARS-CoV-2 infection in ciliated cells
date = 2021
pages = 18
extension = .pdf
mime = application/pdf
words = 6841
sentences = 912
flesch = 71
summary = proteases, may cleave the furin site of SARS-CoV-2 S protein and subunits of epithelial sodium channels ( 15 TMPRSS2, and ACE2 were significantly upregulated in severe COVID-19 patients and SARS-CoV-2 infected 22 Plasmin cleaves the furin site in SARS-CoV S protein (Kam et al. lung epithelial cells and whether SARS-CoV-2 infection alters their expression at the single-cell level. severe/moderate COVID-19 patients and SARS-CoV-2 infected cell lines, mainly owning to ciliated cells. The expression levels of proteases (PLAU, FURIN, TMPRSS2, PLG), ACE2, and SCNN1G in 11 cell 88 Expression levels of PLAU, SCNN1G, and ACE2 in SARS-CoV-2 infection 93 epithelial cell lines infected with SARS-CoV-2: A549, Calu-3, and NHBE (Blanco-Melo et al. CoV-2 infection also increased the expression level of ACE2 in A549 cells (P < 0.05) (Smith et al. Our data showed that the respiratory cells co-express SARS-CoV-2 receptor, ENaC 137 Changes of proteases, ACE2, and SCNN1G in respiratory cell lines after SARS-CoV-2
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id = 10_1101-2021_01_07_425794
author = Chisanga, David
title = Impact of gene annotation choice on the quantification of RNA-seq data
date = 2021
pages = 24
extension = .pdf
mime = application/pdf
words = 9715
sentences = 1233
flesch = 66
summary = Despite the importance of gene annotations in RNA-seq data analysis, very little research has been conducted to examine how differences in annotations impact on gene compared the effect of human genome annotations from popular databases including Ensembl, GENCODE and RefSeq on various aspects of RNA-seq analysis and they showed gene-level expression quantification in an RNA-seq data analysis pipeline. The Ensembl, RefSeq-NCBI and RefSeqRsubread annotations were provided to featureCounts to generate read counts for genes Gene expression data generated using TaqMan RT-PCR and Illumina's BeadChip microarray were used to validate the gene-level quantification results from the RNA-seq The Ensembl and NCBI RefSeq annotations are among the most widely used gene annotations that have been utilized for RNA-seq gene expression quantification in the field. led to a better concordance in gene expression between the RNA-seq data and the RTPCR data, compared to the use of Ensembl and RefSeq-NCBI annotations.
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id = 10_1101-2021_01_06_425569
author = Chen, Li
title = Metabolite discovery through global annotation of untargeted metabolomics data
date = 2021
pages = 31
extension = .pdf
mime = application/pdf
words = 11555
sentences = 1084
flesch = 59
summary = to yeast and mouse data, we identify a half-dozen novel metabolites, including thiamine and taurine Peak annotation occurs in a single global optimization step, based on linear programming, connected nodes matches the atom mass difference and (ii) only co-eluting peaks are connected by edges receive a positive score for MS2 spectra similarity match between the connected nodes, and With a score assigned for each potential node and edge annotation, we formulate the global network A final edge annotation score S( 𝑢, 𝑣, 𝑎 , 𝑏 , 𝐷 ) for choosing candidate formula 𝑎 for node u, A final edge annotation score S( 𝑢, 𝑣, 𝑎 , 𝑏 , 𝐷 ) for choosing candidate formula 𝑎 for node u, A global network optimization approach for untargeted metabolomics data annotation NetID applies global optimization for metabolomics data annotation and metabolite A global network optimization approach for untargeted metabolomics data annotation (NetID).
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id = 10_1101-2021_01_06_425550
author = Chen, Nae-Chyun
title = Improving variant calling using population data and deep learning
date = 2021
pages = 17
extension = .pdf
mime = application/pdf
words = 6912
sentences = 754
flesch = 55
summary = We further evaluated the performance of the models using two whole-exome sequencing (WES) datasets from a recently released set of genome and exome data [23] (Figure 2). Among the populationresolved false-positive errors, more than two third (71.0%) are uncommon (allele frequency ≤ 5%) among the 1000Genomes samples, whereas there are only 11.4% uncommon variants for population-induced false positives. This observation supports the hypothesis that the population-aware model uses allele frequency to adjust its variant calls. A potential concern for population-aware variant calling models is increasing false negative rate for novel alleles. To better understand the zero-frequency variants, we called variants using the DeepVariant PacBio model with the PrecisionFDA v2 35x HG003 reads set sequenced with the We evaluate potential biases introduced by population information in variant calling by comparing population-aware models that use allele frequencies from different Despite greater overall accuracy, we note that the population-aware model underperforms on variants with zero allele frequencies in 1000Genomes.
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id = 10_1101-2020_12_26_424429
author = Nayar, Gowri
title = Analysis and Forecasting of Global RT-PCR Primers for SARS-CoV-2
date = 2021
pages = 13
extension = .pdf
mime = application/pdf
words = 4921
sentences = 407
flesch = 61
summary = SARS-CoV-2 primers in use today by measuring the number of mismatches between primer sequence and genome targets with respect to the sequenced SARS-CoV-2 genomes, we can measure how the targeted proteins are mutating. primer sequences and protocols developed for six different regions – USA, Germany, China, Hong Kong, Japan, and Thailand – percent of genomes hit by each PCR test, labelled by the country and target gene region. Figure 6 shows the average number of mismatches over time, grouped by the genomes sampled The results of this study also demonstrate that each primer target develops a different number of mismatches over time The mutations that lead to mismatches between gene PCR primers and their targets reflect the sequence evolution of the consistent with the observed increasing number of mismatches over time, and shows that evolution of SARS-CoV-2 genomes is of the RdRp gene, primer target creates a disproportionate number of mismatches when compared to genomes sequenced within
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id = 10_1101-2021_01_05_425384
author = Halilaj, Iva
title = Covid19Risk.ai: An open source repository and online calculator of prediction models for early diagnosis and prognosis of Covid-19
date = 2021
pages = 17
extension = .pdf
mime = application/pdf
words = 5110
sentences = 1327
flesch = -15
summary = of COVID-19 patients, expediting the models' transition from research to clinical practice. The open source website https://covid19risk.ai/ currently incorporates nine models from six an inclusive platform for predictive models related to COVID-19. supplement their judgment with patient-specific predictions from externally-validated models Keywords: Covid-19, predictive models, diagnosis, prognosis, nomogram, machine We, as researchers working on COVID-19 models, saw an urgent need for a web-based Our aim for this platform is to include validated prediction models (TRIPOD type 2b and 3) published AI prediction models related to all aspects of COVID-19, including diagnosis, COVID-19 predictive models will serve as a decision aid for doctors. Decision Support System for Severity Risk Prediction and Triage of COVID-19 Patients Covid19Risk.ai: An open source repository and online calculator of prediction models for early diagnosis and prognosis of Covid-19 Covid19Risk.ai: An open source repository and online calculator of prediction models for early diagnosis and prognosis of Covid-19
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id = 10_1101-2021_01_07_425697
author = Luo, Yin
title = Capsule network for protein ubiquitination site prediction
date = 2021
pages = 14
extension = .pdf
mime = application/pdf
words = 5391
sentences = 552
flesch = 59
summary = Capsule network for protein ubiquitination site prediction Capsule network for protein ubiquitination site years, some calculation methods have been developed to predict potential ubiquitination sites. this paper, a deep learning model, "Caps-Ubi," is proposed that uses a capsule network for protein network layer are used as a feature extractor to obtain the functional domains in the protein Data of protein ubiquitination sites the amino acid sequence around the protein ubiquitination site; namely, one-of-K encoding and the performance of various window sizes in one-of-21 and amino acid continuous encoding modes. modes is the best on the capsule network: this proposed Caps-Ubi model achieved an accuracy, In this paper, a new deep learning model for predicting protein ubiquitination sites is proposed, ubiquitination sites in proteins. Large-scale prediction of protein ubiquitination sites machine learning method with substrate motifs to predict ubiquitin-conjugation site on machine learning method with substrate motifs to predict ubiquitin-conjugation site on
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id = 10_1101-2021_01_04_425315
author = Chen, Lulu
title = Sample-wise unsupervised deconvolution of complex tissues
date = 2021
pages = 29
extension = .pdf
mime = application/pdf
words = 7333
sentences = 1028
flesch = 66
summary = tool enables statistically-principled subtype-level downstream analyses, such as detecting subtypespecific differentially expressed genes (sDEG) and differential dependency networks (DDN) nuclear-norm regularized low-rank matrix factorization problem (Wang, Hoffman et al. regularization to optimize the estimation of between-sample variations in each subtype to recover sample-specific deconvolution and optimization solver used in swCAM algorithm, followed by Sample-specific deconvolution problem formulation and the assumption of hidden low-rank pattern CAM-estimated subtype-specific expression matrix serves as the initial reference 𝑺. The objective function of swCAM for sample-specific deconvolution problem and its reformulation As swCAM focuses on subtype-specific variation estimation, simulating biological variance The observations for 300 genes in 50 samples were simulated with subtype-specific expression Gene co-expressed function modules detected by WGCNA on swCAM estimated sample-specific Gene co-expressed function modules detected by WGCNA on swCAM estimated samplespecific expression for each subtype with λ=5 and δ=1 or 0.1. capacity of swCAM to estimate sample-specific signals in each subtype using simulations where
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id = 10_1101-332965
author = Banerjee, Arpita
title = Identification and design of vinyl sulfone inhibitors against Cryptopain-1 – a cysteine protease from cryptosporidiosis-causing Cryptosporidium parvum
date = 2021
pages = 30
extension = .pdf
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words = 9346
sentences = 1457
flesch = 72
summary = protease well by making favorable interactions with important residues of the enzyme. Keywords: Vinyl sulfone inhibitors, Cryptopain-1, Cysteine protease, Molecular residues that were contacted by ligand subgroups across the enzymatic cleft, in one or The ligand subgroup-contacting residues in each complex had been mutated to Alanine; favorably interacting subsite residues (derived from Supplementary Table 1) in the Interactions: enzyme subsite residues ligand subgroups with the ligand ring systems showed highly positive ddGbind values for thiophen group in always, showed favorable interactions even when no ligand group was placed near it. with no ligand group placed near the residue, the interactions were unfavorable. around the ligand subgroups of the best-scored vinyl sulfones compounds (PubChem IDs Figure1: Illustration of the typical binding of vinyl sulfone inhibitors to cysteine protease enzymes. Figure 3: All the residues that are contacted by one or more ligands in the docked complexes of
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id = 10_1101-2021_01_04_425250
author = Thibodeau, Asa
title = A read count-based method to detect multiplets and their cellular origins from snATAC-seq data
date = 2021
pages = 32
extension = .pdf
mime = application/pdf
words = 9658
sentences = 910
flesch = 59
summary = A read count-based method to detect multiplets and their cellular origins from snATAC-seq data Similar to other droplet-based single cell assays, single nucleus ATAC-seq (snATAC-seq) data harbor multiplets 17 found that when snATAC-seq samples were adequately sequenced (e.g., >20k valid read pairs per cell), ATAC-52 ATAC-DoubletDetector detected heterotypic multiplets introduced in PBMC samples with high recall 126 detected multiplets were homotypic (76.7-84.3% in islets, 63-78.7% in PBMCs), with cell types being distributed 207 with respect to their cell proportions for both homotypic and heterotypic multiplet types (Fig. 5d-e, Extended Data 208 ATAC-DoubletDetector for identifying multiplets from snATAC-seq data with enough reads per nuclei, it can also 239 Fig. 2: ATAC-DoubletDetector identifies heterotypic and homotypic multiplets in human PBMC snATAC-seq data. e, The number of cells and percentage of multiplets detected by ATAC-DoubletDetector in PBMC and islet samples. Extended Data Fig. 6: ATAC-DoubletDetector detects both homotypic and heterotypic multiplets at high read depth.
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id = 10_1101-2020_01_29_925354
author = Bartoszewicz, Jakub M.
title = Interpretable detection of novel human viruses from genome sequencing data
date = 2021
pages = 14
extension = .pdf
mime = application/pdf
words = 13578
sentences = 1392
flesch = 61
summary = As the state-of-the art approach for the openview detection of pathogens is genome sequencing (5, 6), it learning (17) to predict host range for a small set of three wellstudied species directly from viral sequences. predicting whether a new virus can potentially infect humans. boundary separates human viruses from other DNA sequences generated the reads from the genomes of human-infecting constituting reads yields a prediction for the whole sequence. predictions from all the reads originating from a given genome In the Fig. 1 we present example filters, visualized as "maxcontrib" sequence logos based on mean partial Shapley values prediction directly from next-generation sequencing reads Three receptor-binding domains (RBDs) are colored in blue, white and red according to the predicted infectious potential of the corresponding genomic sequence. Interpretable detection of novel human viruses from genome sequencing data Interpretable detection of novel human viruses from genome sequencing data
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id = 10_1101-2020_09_09_289074
author = Ortuso, Francesco
title = Structural Genetics of circulating variants affecting the SARS-CoV-2 Spike / human ACE2 complex
date = 2021
pages = 23
extension = .pdf
mime = application/pdf
words = 7878
sentences = 977
flesch = 67
summary = Structural Genetics of circulating variants affecting the SARS-CoV-2 Spike / human ACE2 complex SARS-CoV-2, COVID-19, mutations, Spike, ACE2 SARS-CoV-2 entry in human cells is mediated by the interaction between the viral Spike protein and protein variants in the SARS-CoV-2 population as the result of mutations, and it is unclear if these SARS-CoV-2 (the COVID-19 virus) and human cells, through the analysis of Spike/ACE2 complexes. future mutations targeting the ACE2/Spike binding and detected by sequencing SARS-CoV-2 on a We obtained structural models of the SARS-CoV-2 Spike interacting with the human ACE2 from three contributing to the interaction between Spike and ACE2, according to GBPM (see Table 1 and Fig 3 for A less frequent mutation amongst those predicted to contribute to the ACE2/Spike interaction is population and non-zero GBPM average score in the ACE2/Spike interaction models. ACE2 variants with non-zero GBPM score in the Spike interaction model.
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id = 10_1101-2021_01_06_425546
author = Osthus, Dave
title = Fast and Accurate Influenza Forecasting in the United States with Inferno
date = 2021
pages =
extension = .pdf
mime = application/xhtml+xml
words = 72
sentences = 16
flesch = 15
summary = bioRxiv.org the preprint server for Biology Skip to main content Home Submit ALERTS / RSS Search for this keyword Advanced Search Subject Areas All Articles Animal Behavior and Cognition Biochemistry Bioengineering Bioinformatics Biophysics Cancer Biology Cell Biology Clinical Trials Developmental Biology Ecology Epidemiology Evolutionary Biology Genetics Genomics Immunology Microbiology Molecular Biology Neuroscience Paleontology Pathology Pharmacology and Toxicology Physiology Plant Biology Scientific Communication and Education Synthetic Biology Systems Biology Zoology View by Month
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id = 10_1101-2020_08_28_271981
author = He, Jiahua
title = Full-length de novo protein structure determination from cryo-EM maps using deep learning
date = 2021
pages = 26
extension = .pdf
mime = application/pdf
words = 10020
sentences = 1169
flesch = 72
summary = Full-length de novo protein structure determination from cryo-EM maps using deep learning structure types were predicted by a second DenseNet. Finally, the protein sequence was aligned to the main-chain according to the predicted Cα probabilities, amino acid types, and secondary structure amino acid type, and secondary structure type for each main-chain point, the target protein sequence The second network (i.e. DenseNet B) is used to predict the amino acid type and secondary structure type of a main-chain local dense point (LDP). Figure 3 shows a comparison of the predicted Cα models for the protein chains of different lengths The authors acknowledge professor Daisuke Kihara and his students Genki Terashi and Sai Raghavendra Maddhuri Venkata Subramaniya from Purdue University for providing their datasets. A New Protocol for Atomic-Level Protein Structure Modeling and Refinement Using Low-to-Medium Resolution Cryo-EM Density Maps. Figure 8: Protein models reconstructed by DeepMM and Phenix for the Chain A of 6DW1
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id = 10_1101-436634
author = Cotto, Kelsy C.
title = RegTools: Integrated analysis of genomic and transcriptomic data for the discovery of splicing variants in cancer
date = 2021
pages = 48
extension = .pdf
mime = application/pdf
words = 14993
sentences = 1337
flesch = 59
summary = RegTools: Integrated analysis of genomic and transcriptomic data for the discovery of splicing variants in cancer somatic variants from genomic data with splice junctions from transcriptomic data to identify isoforms, we annotated them with the Variant Effect Predictor (VEP), SpliceAI, and GenotypeTissue Expression (GTEx) junction counts and compared our results to other tools that integrate tools, the unbiased nature of RegTools has allowed us to identify novel splice variants and identify potential cis-acting splice-relevant variants in tumors (www.regtools.org). To demonstrate the utility of RegTools in identifying potential splice-relevant variants from tumor transcriptome as described above and its associated variants based on splice junction region For our analysis, we annotated the pairs of associated variants and junctions identified by Pan-cancer analysis of 35 tumor types identifies somatic variants that alter canonical We also identify recurrent splice altering variants in genes not known to be cancer genes RegTools contains three sub-modules: "variants", "junctions", and "cis-splice-effects".
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10_1101-2021_01_08_425885
10_1101-2021_01_08_425379
10_1101-2020_12_14_422697
10_1101-2021_01_08_425885
number of items: 50
sum of words: 175,544
average size in words: 7,632
average readability score: 52
nouns: preprint; cell; data; version; author; review; copyright; holder; funder; peer; preprintthis; %; cells; license; genes; perpetuity; gene; analysis; number; expression; sequences; cancer; sequence; model; type; fig; samples; p; figure; time; rights; reuse; permission; species; types; licenseavailable; methods; seq; models; values; results; information; licensemade; sample; datasets; lines; b; score; dataset; protein
verbs: is; was; are; were; be; posted; certified; has; using; display; used; granted; biorxiv; based; have; made; allowed; reserved; found; �; shown; associated; been; see; compared; identified; obtained; given; known; applied; showed; including; had; generated; calculated; predicted; selected; identify; read; observed; performed; defined; set; shows; derived; computed; included; according; estimated; use
adjectives: -; available; different; single; other; international; human; high; same; specific; such; non; more; new; average; multiple; higher; low; first; similar; scalar; significant; variant; relative; negative; biological; positive; genomic; large; functional; common; clinical; regulatory; novel; �; genome; total; several; experimental; subclonal; individual; small; true; top; deep; many; additional; general; wide; main
adverbs: not; also; then; only; more; however; well; respectively; most; as; thus; therefore; here; further; significantly; even; e.g.; first; highly; out; at; next; very; finally; often; specifically; least; together; still; previously; randomly; directly; less; much; relatively; above; instead; particularly; so; potentially; up; hence; fully; differentially; furthermore; currently; moreover; http://creativecommons.org/licenses/by-nc-nd/4.0/; recently; rather
pronouns: we; it; our; their; i; its; they; them; us; itself; https://doi.org/10.1101/2021.01.06.425569; https://doi.org/10.1101/2021.01.08.425918; his; one; swcam; adroit; he; em; https://doi.org/10.1101/2021.01.04.425335; themselves; https://doi.org/10.1101/2021.01.07.425716; bl; http://paperpile.com/b/h8ctd0/cq1b; her; ng; il-27ra; you; yj; mine; λ; us-; ua; u; she; s; ourselves; n; matchdrugwithdisease; m; https://doi.org/10.1016/j.cell.2011.02.013; 𝜆𝜃; 𝜆; 𝒗𝑖; 𝑺; 𝑢-; 𝑘1; 𝑖𝑗; 𝑓; 𝑉-; ―
proper nouns: january; al; et; �; ⋅; j.; m.; nc; rna; s.; c; nd; international; figure; sars; m; −; a.; il-27; s; r.; d.; c.; cov-2; l.; e.; fig; j; p.; p; h.; b.; k.; t.; supplementary; g.; b; a; methods; y.; d; e; .; t; n; g; cell; hla; by; k
keywords: january; international; figure; sars; rna; fig; cell; biology; variant; supp; gwas; genome; gene; dna; disease; datum; cancer; blast; ace2; 𝐷27; 𝐷23; vntr; vae; usc; uclust; type; tumor; trajectory; toronto; time; table; t69; sørensen; supplementary; subclonal; study; stat1; srs; spike; simlr; sequence; section; sec; rsubread; rpgg; rpe1; rmsd; ricotta; q18; prescription
one topic; one dimension: 10
file(s): ./cache/10_1101-2021_01_08_425887.pdf
titles(s): Auto-CORPus: Automated and Consistent Outputs from Research Publications
three topics; one dimension: org; com; 10
file(s): , , ./cache/10_1101-2021_01_06_425560.pdf
titles(s): Computing the Riemannian curvature of image patch and single-cell RNA sequencing data manifolds using extrinsic differential geometry | Competitive binding of STATs to receptor phospho-Tyr motifs accounts for altered cytokine responses in autoimmune disorders | Review and performance evaluation of trait-based between-community dissimilarity measures
five topics; three dimensions: http com paperpile; org 10 2021; 10 org https; cell 10 doi; 10 org doi
file(s): , , , ,
titles(s): Competitive binding of STATs to receptor phospho-Tyr motifs accounts for altered cytokine responses in autoimmune disorders | Evaluating the transcriptional fidelity of cancer models | Integrated cross-study datasets of genetic dependencies in cancer | AdRoit: an accurate and robust method to infer complex transcriptome composition | LiquidCNA: tracking subclonal evolution from longitudinal liquid biopsies using somatic copy number alterations
Type: biorxiv
title: bioinformatics-from-biorxiv
date: 2021-01-10
time: 13:51
username: emorgan
patron: Eric Morgan
email: emorgan@nd.edu
input: vUYT7OIQgp.xml
==== make-pages.sh htm files
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id: 10_1101-2021_01_08_425976
author: Ahuja, Yuri
title: Semi-supervised Calibration of Risk with Noisy Event Times (SCORNET) Using Electronic Health Record Data
date: 2021
words: 72.0
sentences: 16.0
pages:
flesch: 15.0
cache: ./cache/10_1101-2021_01_08_425976.pdf
txt: ./txt/10_1101-2021_01_08_425976.txt
summary: bioRxiv.org the preprint server for Biology Skip to main content Home Submit ALERTS / RSS Search for this keyword Advanced Search Subject Areas All Articles Animal Behavior and Cognition Biochemistry Bioengineering Bioinformatics Biophysics Cancer Biology Cell Biology Clinical Trials Developmental Biology Ecology Epidemiology Evolutionary Biology Genetics Genomics Immunology Microbiology Molecular Biology Neuroscience Paleontology Pathology Pharmacology and Toxicology Physiology Plant Biology Scientific Communication and Education Synthetic Biology Systems Biology Zoology View by Month
id: 10_1101-2021_01_06_425581
author: Alicea, Bradly J
title: Periodicity in the embryo: emergence of order in space, diffusion of order in time
date: 2021
words: nan
sentences: nan
pages:
flesch: nan
cache:
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id: 10_1101-2021_01_07_425637
author: Arneson, Adriana
title: A mammalian methylation array for profiling methylation levels at conserved sequences
date: 2021
words: 12153.0
sentences: 1117.0
pages: 39
flesch: 58.0
cache: ./cache/10_1101-2021_01_07_425637.pdf
txt: ./txt/10_1101-2021_01_07_425637.txt
summary: characterize the CpGs on the mammalian methylation array with various genomic annotations. Array probes are sequences of length 50bp flanking a target CpG based on the human reference We added probes targeting 1986 CpGs to the mammalian methylation array based on All 37488 CpGs profiled on the mammalian methylation array apply to humans, but only a CpGs on the mammalian array cover 6871 human and 5659 mouse genes when each DNA methylation samples for three species: human (n=10 arrays), mouse (n=20), and rat (n=15), synthetic DNA data from 3 species: human (n=10 mammalian arrays), mouse (n=20), and rat CpG and gene coverage of probes on the mammalian methylation array across CpG island and chromatin state analysis of mammalian methylation probes. probes targeting the same CpG that can also be found on the human EPIC array that were not mammalian methylation array to the human (hg19) and mouse (mm10) genome using QUASR
id: 10_1101-2020_10_26_351783
author: Badam, Tejaswi V.S.
title: A validated generally applicable approach using the systematic assessment of disease modules by GWAS reveals a multi-omic module strongly associated with risk factors in multiple sclerosis
date: 2021
words: nan
sentences: nan
pages:
flesch: nan
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id: 10_1101-2021_01_06_425494
author: Baldwin, Quenisha
title: The topological free energy of proteins
date: 2021
words: 72.0
sentences: 16.0
pages:
flesch: 15.0
cache: ./cache/10_1101-2021_01_06_425494.pdf
txt: ./txt/10_1101-2021_01_06_425494.txt
summary: bioRxiv.org the preprint server for Biology Skip to main content Home Submit ALERTS / RSS Search for this keyword Advanced Search Subject Areas All Articles Animal Behavior and Cognition Biochemistry Bioengineering Bioinformatics Biophysics Cancer Biology Cell Biology Clinical Trials Developmental Biology Ecology Epidemiology Evolutionary Biology Genetics Genomics Immunology Microbiology Molecular Biology Neuroscience Paleontology Pathology Pharmacology and Toxicology Physiology Plant Biology Scientific Communication and Education Synthetic Biology Systems Biology Zoology View by Month
id: 10_1101-332965
author: Banerjee, Arpita
title: Identification and design of vinyl sulfone inhibitors against Cryptopain-1 – a cysteine protease from cryptosporidiosis-causing Cryptosporidium parvum
date: 2021
words: 9346.0
sentences: 1457.0
pages: 30
flesch: 72.0
cache: ./cache/10_1101-332965.pdf
txt: ./txt/10_1101-332965.txt
summary: protease well by making favorable interactions with important residues of the enzyme. Keywords: Vinyl sulfone inhibitors, Cryptopain-1, Cysteine protease, Molecular residues that were contacted by ligand subgroups across the enzymatic cleft, in one or The ligand subgroup-contacting residues in each complex had been mutated to Alanine; favorably interacting subsite residues (derived from Supplementary Table 1) in the Interactions: enzyme subsite residues ligand subgroups with the ligand ring systems showed highly positive ddGbind values for thiophen group in always, showed favorable interactions even when no ligand group was placed near it. with no ligand group placed near the residue, the interactions were unfavorable. around the ligand subgroups of the best-scored vinyl sulfones compounds (PubChem IDs Figure1: Illustration of the typical binding of vinyl sulfone inhibitors to cysteine protease enzymes. Figure 3: All the residues that are contacted by one or more ligands in the docked complexes of
id: 10_1101-2020_01_29_925354
author: Bartoszewicz, Jakub M.
title: Interpretable detection of novel human viruses from genome sequencing data
date: 2021
words: 13578.0
sentences: 1392.0
pages: 14
flesch: 61.0
cache: ./cache/10_1101-2020_01_29_925354.pdf
txt: ./txt/10_1101-2020_01_29_925354.txt
summary: As the state-of-the art approach for the openview detection of pathogens is genome sequencing (5, 6), it learning (17) to predict host range for a small set of three wellstudied species directly from viral sequences. predicting whether a new virus can potentially infect humans. boundary separates human viruses from other DNA sequences generated the reads from the genomes of human-infecting constituting reads yields a prediction for the whole sequence. predictions from all the reads originating from a given genome In the Fig. 1 we present example filters, visualized as "maxcontrib" sequence logos based on mean partial Shapley values prediction directly from next-generation sequencing reads Three receptor-binding domains (RBDs) are colored in blue, white and red according to the predicted infectious potential of the corresponding genomic sequence. Interpretable detection of novel human viruses from genome sequencing data Interpretable detection of novel human viruses from genome sequencing data
id: 10_1101-2021_01_08_425967
author: Camacho-Hernández, Diego A.
title: Partition Quantitative Assessment (PQA): A quantitative methodology to assess the embedded noise in clustered omics and systems biology data
date: 2021
words: nan
sentences: nan
pages:
flesch: nan
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id: 10_1101-2021_01_07_425716
author: Cao, Yingying
title: Comprehensive comparison of transcriptomes in SARS-CoV-2 infection: alternative entry routes and innate immune responses
date: 2021
words: nan
sentences: nan
pages:
flesch: nan
cache:
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summary:
id: 10_1101-2021_01_06_425569
author: Chen, Li
title: Metabolite discovery through global annotation of untargeted metabolomics data
date: 2021
words: 11555.0
sentences: 1084.0
pages: 31
flesch: 59.0
cache: ./cache/10_1101-2021_01_06_425569.pdf
txt: ./txt/10_1101-2021_01_06_425569.txt
summary: to yeast and mouse data, we identify a half-dozen novel metabolites, including thiamine and taurine Peak annotation occurs in a single global optimization step, based on linear programming, connected nodes matches the atom mass difference and (ii) only co-eluting peaks are connected by edges receive a positive score for MS2 spectra similarity match between the connected nodes, and With a score assigned for each potential node and edge annotation, we formulate the global network A final edge annotation score S( 𝑢, 𝑣, 𝑎 , 𝑏 , 𝐷 ) for choosing candidate formula 𝑎 for node u, A final edge annotation score S( 𝑢, 𝑣, 𝑎 , 𝑏 , 𝐷 ) for choosing candidate formula 𝑎 for node u, A global network optimization approach for untargeted metabolomics data annotation NetID applies global optimization for metabolomics data annotation and metabolite A global network optimization approach for untargeted metabolomics data annotation (NetID).
id: 10_1101-2021_01_04_425315
author: Chen, Lulu
title: Sample-wise unsupervised deconvolution of complex tissues
date: 2021
words: 7333.0
sentences: 1028.0
pages: 29
flesch: 66.0
cache: ./cache/10_1101-2021_01_04_425315.pdf
txt: ./txt/10_1101-2021_01_04_425315.txt
summary: tool enables statistically-principled subtype-level downstream analyses, such as detecting subtypespecific differentially expressed genes (sDEG) and differential dependency networks (DDN) nuclear-norm regularized low-rank matrix factorization problem (Wang, Hoffman et al. regularization to optimize the estimation of between-sample variations in each subtype to recover sample-specific deconvolution and optimization solver used in swCAM algorithm, followed by Sample-specific deconvolution problem formulation and the assumption of hidden low-rank pattern CAM-estimated subtype-specific expression matrix serves as the initial reference 𝑺. The objective function of swCAM for sample-specific deconvolution problem and its reformulation As swCAM focuses on subtype-specific variation estimation, simulating biological variance The observations for 300 genes in 50 samples were simulated with subtype-specific expression Gene co-expressed function modules detected by WGCNA on swCAM estimated sample-specific Gene co-expressed function modules detected by WGCNA on swCAM estimated samplespecific expression for each subtype with λ=5 and δ=1 or 0.1. capacity of swCAM to estimate sample-specific signals in each subtype using simulations where
id: 10_1101-2021_01_06_425550
author: Chen, Nae-Chyun
title: Improving variant calling using population data and deep learning
date: 2021
words: 6912.0
sentences: 754.0
pages: 17
flesch: 55.0
cache: ./cache/10_1101-2021_01_06_425550.pdf
txt: ./txt/10_1101-2021_01_06_425550.txt
summary: We further evaluated the performance of the models using two whole-exome sequencing (WES) datasets from a recently released set of genome and exome data [23] (Figure 2). Among the populationresolved false-positive errors, more than two third (71.0%) are uncommon (allele frequency ≤ 5%) among the 1000Genomes samples, whereas there are only 11.4% uncommon variants for population-induced false positives. This observation supports the hypothesis that the population-aware model uses allele frequency to adjust its variant calls. A potential concern for population-aware variant calling models is increasing false negative rate for novel alleles. To better understand the zero-frequency variants, we called variants using the DeepVariant PacBio model with the PrecisionFDA v2 35x HG003 reads set sequenced with the We evaluate potential biases introduced by population information in variant calling by comparing population-aware models that use allele frequencies from different Despite greater overall accuracy, we note that the population-aware model underperforms on variants with zero allele frequencies in 1000Genomes.
id: 10_1101-2021_01_07_425794
author: Chisanga, David
title: Impact of gene annotation choice on the quantification of RNA-seq data
date: 2021
words: 9715.0
sentences: 1233.0
pages: 24
flesch: 66.0
cache: ./cache/10_1101-2021_01_07_425794.pdf
txt: ./txt/10_1101-2021_01_07_425794.txt
summary: Despite the importance of gene annotations in RNA-seq data analysis, very little research has been conducted to examine how differences in annotations impact on gene compared the effect of human genome annotations from popular databases including Ensembl, GENCODE and RefSeq on various aspects of RNA-seq analysis and they showed gene-level expression quantification in an RNA-seq data analysis pipeline. The Ensembl, RefSeq-NCBI and RefSeqRsubread annotations were provided to featureCounts to generate read counts for genes Gene expression data generated using TaqMan RT-PCR and Illumina''s BeadChip microarray were used to validate the gene-level quantification results from the RNA-seq The Ensembl and NCBI RefSeq annotations are among the most widely used gene annotations that have been utilized for RNA-seq gene expression quantification in the field. led to a better concordance in gene expression between the RNA-seq data and the RTPCR data, compared to the use of Ensembl and RefSeq-NCBI annotations.
id: 10_1101-436634
author: Cotto, Kelsy C.
title: RegTools: Integrated analysis of genomic and transcriptomic data for the discovery of splicing variants in cancer
date: 2021
words: 14993.0
sentences: 1337.0
pages: 48
flesch: 59.0
cache: ./cache/10_1101-436634.pdf
txt: ./txt/10_1101-436634.txt
summary: RegTools: Integrated analysis of genomic and transcriptomic data for the discovery of splicing variants in cancer somatic variants from genomic data with splice junctions from transcriptomic data to identify isoforms, we annotated them with the Variant Effect Predictor (VEP), SpliceAI, and GenotypeTissue Expression (GTEx) junction counts and compared our results to other tools that integrate tools, the unbiased nature of RegTools has allowed us to identify novel splice variants and identify potential cis-acting splice-relevant variants in tumors (www.regtools.org). To demonstrate the utility of RegTools in identifying potential splice-relevant variants from tumor transcriptome as described above and its associated variants based on splice junction region For our analysis, we annotated the pairs of associated variants and junctions identified by Pan-cancer analysis of 35 tumor types identifies somatic variants that alter canonical We also identify recurrent splice altering variants in genes not known to be cancer genes RegTools contains three sub-modules: "variants", "junctions", and "cis-splice-effects".
id: 10_1101-2021_01_05_425409
author: Dholakia, Dhwani
title: HLA-SPREAD: A comprehensive resource for HLA associated diseases, drug reactions and SNPs across populations
date: 2021
words: nan
sentences: nan
pages:
flesch: nan
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summary:
id: 10_1101-2021_01_05_425384
author: Halilaj, Iva
title: Covid19Risk.ai: An open source repository and online calculator of prediction models for early diagnosis and prognosis of Covid-19
date: 2021
words: 5110.0
sentences: 1327.0
pages: 17
flesch: -15.0
cache: ./cache/10_1101-2021_01_05_425384.pdf
txt: ./txt/10_1101-2021_01_05_425384.txt
summary: of COVID-19 patients, expediting the models'' transition from research to clinical practice. The open source website https://covid19risk.ai/ currently incorporates nine models from six an inclusive platform for predictive models related to COVID-19. supplement their judgment with patient-specific predictions from externally-validated models Keywords: Covid-19, predictive models, diagnosis, prognosis, nomogram, machine We, as researchers working on COVID-19 models, saw an urgent need for a web-based Our aim for this platform is to include validated prediction models (TRIPOD type 2b and 3) published AI prediction models related to all aspects of COVID-19, including diagnosis, COVID-19 predictive models will serve as a decision aid for doctors. Decision Support System for Severity Risk Prediction and Triage of COVID-19 Patients Covid19Risk.ai: An open source repository and online calculator of prediction models for early diagnosis and prognosis of Covid-19 Covid19Risk.ai: An open source repository and online calculator of prediction models for early diagnosis and prognosis of Covid-19
id: 10_1101-2020_08_28_271981
author: He, Jiahua
title: Full-length de novo protein structure determination from cryo-EM maps using deep learning
date: 2021
words: 10020.0
sentences: 1169.0
pages: 26
flesch: 72.0
cache: ./cache/10_1101-2020_08_28_271981.pdf
txt: ./txt/10_1101-2020_08_28_271981.txt
summary: Full-length de novo protein structure determination from cryo-EM maps using deep learning structure types were predicted by a second DenseNet. Finally, the protein sequence was aligned to the main-chain according to the predicted Cα probabilities, amino acid types, and secondary structure amino acid type, and secondary structure type for each main-chain point, the target protein sequence The second network (i.e. DenseNet B) is used to predict the amino acid type and secondary structure type of a main-chain local dense point (LDP). Figure 3 shows a comparison of the predicted Cα models for the protein chains of different lengths The authors acknowledge professor Daisuke Kihara and his students Genki Terashi and Sai Raghavendra Maddhuri Venkata Subramaniya from Purdue University for providing their datasets. A New Protocol for Atomic-Level Protein Structure Modeling and Refinement Using Low-to-Medium Resolution Cryo-EM Density Maps. Figure 8: Protein models reconstructed by DeepMM and Phenix for the Chain A of 6DW1
id: 10_1101-2021_01_07_425801
author: Hou, Yapeng
title: Fibrinolysis influences SARS-CoV-2 infection in ciliated cells
date: 2021
words: 6841.0
sentences: 912.0
pages: 18
flesch: 71.0
cache: ./cache/10_1101-2021_01_07_425801.pdf
txt: ./txt/10_1101-2021_01_07_425801.txt
summary: proteases, may cleave the furin site of SARS-CoV-2 S protein and subunits of epithelial sodium channels ( 15 TMPRSS2, and ACE2 were significantly upregulated in severe COVID-19 patients and SARS-CoV-2 infected 22 Plasmin cleaves the furin site in SARS-CoV S protein (Kam et al. lung epithelial cells and whether SARS-CoV-2 infection alters their expression at the single-cell level. severe/moderate COVID-19 patients and SARS-CoV-2 infected cell lines, mainly owning to ciliated cells. The expression levels of proteases (PLAU, FURIN, TMPRSS2, PLG), ACE2, and SCNN1G in 11 cell 88 Expression levels of PLAU, SCNN1G, and ACE2 in SARS-CoV-2 infection 93 epithelial cell lines infected with SARS-CoV-2: A549, Calu-3, and NHBE (Blanco-Melo et al. CoV-2 infection also increased the expression level of ACE2 in A549 cells (P < 0.05) (Smith et al. Our data showed that the respiratory cells co-express SARS-CoV-2 receptor, ENaC 137 Changes of proteases, ACE2, and SCNN1G in respiratory cell lines after SARS-CoV-2
id: 10_1101-2021_01_08_425887
author: Hu, Yan
title: Auto-CORPus: Automated and Consistent Outputs from Research Publications
date: 2021
words: 6886.0
sentences: 553.0
pages: 10
flesch: 53.0
cache: ./cache/10_1101-2021_01_08_425887.pdf
txt: ./txt/10_1101-2021_01_08_425887.txt
summary: the same structured model, so that these can be used as input to rule-based or deep learning algorithms for data extraction. example, at this point in this article the main headers are ''abstract'' followed by ''introduction'' and ''materials and methods'' that could make up a digraph. We use this process to evaluate new potential synonyms for existing terms and identify abstract → introduction → materials → results → discussion → conclusion → acknowledgements → footnotes section → references. Based on the digraph, we then assigned data and data description to be synonyms of the materials section, and participants From the analysis of ego-networks four new potential categories were identified: disclosure, graphical abstract, highlights and participants. Newly identified synonyms for existing IAO terms (00006xx) from the digraph mapping of 2,441 publications. Newly identified synonyms for existing IAO terms (00006xx) from the digraph mapping of 2,441 publications.
id: 10_1101-2021_01_05_425508
author: Iwasaki, Yuki
title: Human cell-dependent, directional, time-dependent changes in the mono- and oligonucleotide compositions of SARS-CoV-2 genomes
date: 2021
words: nan
sentences: nan
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id: 10_1101-2021_01_04_425335
author: Ji, Guoli
title: Learning association for single-cell transcriptomics by integrating profiling of gene expression and alternative polyadenylation
date: 2021
words: nan
sentences: nan
pages:
flesch: nan
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id: 10_1101-2020_12_24_424332
author: Jolly, Bani
title: Genetic epidemiology of variants associated with immune escape from global SARS-CoV-2 genomes
date: 2021
words: nan
sentences: nan
pages:
flesch: nan
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id: 10_1101-2021_01_05_425414
author: Lakatos, Eszter
title: LiquidCNA: tracking subclonal evolution from longitudinal liquid biopsies using somatic copy number alterations
date: 2021
words: nan
sentences: nan
pages:
flesch: nan
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id: 10_1101-2021_01_06_425560
author: Lengyel, Attila
title: Review and performance evaluation of trait-based between-community dissimilarity measures
date: 2021
words: 15548.0
sentences: 2320.0
pages: 50
flesch: 67.0
cache: ./cache/10_1101-2021_01_06_425560.pdf
txt: ./txt/10_1101-2021_01_06_425560.txt
summary: Review and performance evaluation of trait-based between-community dissimilarity measures 2 Review and performance evaluation of trait-based between-community dissimilarity measures 2 2. In this paper we reviewed the trait-based dissimilarity indices available in the 16 dissimilarities calculated by different indices correlate with environmental distances. beta diversity, dissimilarity index, distance metric, community ecology, functional traits 39 including several families of trait-based dissimilarity indices. FDissim indices incorporate trait information into the calculation of dissimilarity in different 162 Indices following this approach represent each community with a typical trait value, and 185 2005) or trait-based dissimilarity of species (Lepš 220 of the similarity indices for presence/absence data disregarding species properties, while the 281 ordinariness values in the species-based (dis-)similarity indices. Ricotta & Pavoine (2015) introduced a new family of trait-based similarity measures called 331 For species-based analyses, Ricotta & Podani (2017) suggested a general formula of distance 336 compared how strongly the dissimilarity indices correlate with the environmental distance 515
id: 10_1101-2021_01_07_425782
author: Lu, Tianyu
title: dynUGENE: an R package for uncertainty-aware gene regulatory network inference, simulation, and visualization
date: 2021
words: nan
sentences: nan
pages:
flesch: nan
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id: 10_1101-2020_08_13_249839
author: Lu, Tsung-Yu
title: Profiling variable-number tandem repeat variation across populations using repeat-pangenome graphs
date: 2021
words: nan
sentences: nan
pages:
flesch: nan
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id: 10_1101-2021_01_07_425697
author: Luo, Yin
title: Capsule network for protein ubiquitination site prediction
date: 2021
words: 5391.0
sentences: 552.0
pages: 14
flesch: 59.0
cache: ./cache/10_1101-2021_01_07_425697.pdf
txt: ./txt/10_1101-2021_01_07_425697.txt
summary: Capsule network for protein ubiquitination site prediction Capsule network for protein ubiquitination site years, some calculation methods have been developed to predict potential ubiquitination sites. this paper, a deep learning model, "Caps-Ubi," is proposed that uses a capsule network for protein network layer are used as a feature extractor to obtain the functional domains in the protein Data of protein ubiquitination sites the amino acid sequence around the protein ubiquitination site; namely, one-of-K encoding and the performance of various window sizes in one-of-21 and amino acid continuous encoding modes. modes is the best on the capsule network: this proposed Caps-Ubi model achieved an accuracy, In this paper, a new deep learning model for predicting protein ubiquitination sites is proposed, ubiquitination sites in proteins. Large-scale prediction of protein ubiquitination sites machine learning method with substrate motifs to predict ubiquitin-conjugation site on machine learning method with substrate motifs to predict ubiquitin-conjugation site on
id: 10_1101-2021_01_05_425266
author: Morales, Manuel A.
title: DeepStrain: A Deep Learning Workflow for the Automated Characterization of Cardiac Mechanics
date: 2021
words: nan
sentences: nan
pages:
flesch: nan
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id: 10_1101-2021_01_08_425952
author: Nash, Anthony
title: rdrugtrajectory: An R Package for the Analysis of Drug Prescriptions in Electronic Health Care Records
date: 2021
words: nan
sentences: nan
pages:
flesch: nan
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id: 10_1101-2020_12_26_424429
author: Nayar, Gowri
title: Analysis and Forecasting of Global RT-PCR Primers for SARS-CoV-2
date: 2021
words: 4921.0
sentences: 407.0
pages: 13
flesch: 61.0
cache: ./cache/10_1101-2020_12_26_424429.pdf
txt: ./txt/10_1101-2020_12_26_424429.txt
summary: SARS-CoV-2 primers in use today by measuring the number of mismatches between primer sequence and genome targets with respect to the sequenced SARS-CoV-2 genomes, we can measure how the targeted proteins are mutating. primer sequences and protocols developed for six different regions – USA, Germany, China, Hong Kong, Japan, and Thailand – percent of genomes hit by each PCR test, labelled by the country and target gene region. Figure 6 shows the average number of mismatches over time, grouped by the genomes sampled The results of this study also demonstrate that each primer target develops a different number of mismatches over time The mutations that lead to mismatches between gene PCR primers and their targets reflect the sequence evolution of the consistent with the observed increasing number of mismatches over time, and shows that evolution of SARS-CoV-2 genomes is of the RdRp gene, primer target creates a disproportionate number of mismatches when compared to genomes sequenced within
id: 10_1101-2021_01_04_425285
author: Nugent, Cameron M.
title: debar, a sequence-by-sequence denoiser for COI-5P DNA barcode data
date: 2021
words: nan
sentences: nan
pages:
flesch: nan
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id: 10_1101-2020_09_09_289074
author: Ortuso, Francesco
title: Structural Genetics of circulating variants affecting the SARS-CoV-2 Spike / human ACE2 complex
date: 2021
words: 7878.0
sentences: 977.0
pages: 23
flesch: 67.0
cache: ./cache/10_1101-2020_09_09_289074.pdf
txt: ./txt/10_1101-2020_09_09_289074.txt
summary: Structural Genetics of circulating variants affecting the SARS-CoV-2 Spike / human ACE2 complex SARS-CoV-2, COVID-19, mutations, Spike, ACE2 SARS-CoV-2 entry in human cells is mediated by the interaction between the viral Spike protein and protein variants in the SARS-CoV-2 population as the result of mutations, and it is unclear if these SARS-CoV-2 (the COVID-19 virus) and human cells, through the analysis of Spike/ACE2 complexes. future mutations targeting the ACE2/Spike binding and detected by sequencing SARS-CoV-2 on a We obtained structural models of the SARS-CoV-2 Spike interacting with the human ACE2 from three contributing to the interaction between Spike and ACE2, according to GBPM (see Table 1 and Fig 3 for A less frequent mutation amongst those predicted to contribute to the ACE2/Spike interaction is population and non-zero GBPM average score in the ACE2/Spike interaction models. ACE2 variants with non-zero GBPM score in the Spike interaction model.
id: 10_1101-2021_01_06_425546
author: Osthus, Dave
title: Fast and Accurate Influenza Forecasting in the United States with Inferno
date: 2021
words: 72.0
sentences: 16.0
pages:
flesch: 15.0
cache: ./cache/10_1101-2021_01_06_425546.pdf
txt: ./txt/10_1101-2021_01_06_425546.txt
summary: bioRxiv.org the preprint server for Biology Skip to main content Home Submit ALERTS / RSS Search for this keyword Advanced Search Subject Areas All Articles Animal Behavior and Cognition Biochemistry Bioengineering Bioinformatics Biophysics Cancer Biology Cell Biology Clinical Trials Developmental Biology Ecology Epidemiology Evolutionary Biology Genetics Genomics Immunology Microbiology Molecular Biology Neuroscience Paleontology Pathology Pharmacology and Toxicology Physiology Plant Biology Scientific Communication and Education Synthetic Biology Systems Biology Zoology View by Month
id: 10_1101-2020_11_13_381475
author: Pabis, Kamil
title: Triplex and other DNA motifs show motif-specific associations with mitochondrial DNA deletions and species lifespan
date: 2021
words: nan
sentences: nan
pages:
flesch: nan
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id: 10_1101-2020_05_22_110247
author: Pacini, Clare
title: Integrated cross-study datasets of genetic dependencies in cancer
date: 2021
words: nan
sentences: nan
pages:
flesch: nan
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id: 10_1101-2020_03_27_012757
author: Peng, Da
title: Evaluating the transcriptional fidelity of cancer models
date: 2021
words: nan
sentences: nan
pages:
flesch: nan
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id: 10_1101-2021_01_08_426008
author: Pipes, Lenore
title: AncestralClust: Clustering of Divergent Nucleotide Sequences by Ancestral Sequence Reconstruction using Phylogenetic Trees
date: 2021
words: 3682.0
sentences: 382.0
pages: 7
flesch: 63.0
cache: ./cache/10_1101-2021_01_08_426008.pdf
txt: ./txt/10_1101-2021_01_08_426008.txt
summary: AncestralClust: Clustering of Divergent Nucleotide Sequences by Ancestral Sequence Reconstruction using Phylogenetic Trees Despite the exponential increase in the size of sequence databases of homologous genes, few methods exist to cluster At low identities, these methods produce uneven clusters where the majority of sequences are are no clustering methods that can accurately cluster large taxonomically divergent metabarcoding reference databases such as databases (Schoch et al., 2020), there is a need for new computationally efficient methods that can cluster divergent sequences. To cluster divergent sequences, we developed AncestralClust clustering methods: UCLUST (Edgar, 2010), meshclust2 (James dataset against UCLUST because it is the most widely used clustering program and it performs better than CD-HIT on low identity We developed a phylogenetic-based clustering method, AncestralClust, specifically to cluster divergent metabarcode sequences. Comparisons of clustering methods using 13,043 COI sequences from 11 different species.
id: 10_1101-2021_01_07_425773
author: Robitaille, Michael C.
title: A Self-Supervised Machine Learning Approach for Objective Live Cell Segmentation and Analysis
date: 2021
words: nan
sentences: nan
pages:
flesch: nan
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id: 10_1101-2021_01_08_425897
author: Soleymanjahi, Saeed
title: APOBEC1 mediated C-to-U RNA editing: target sequence and trans-acting factor contribution to 177 RNA editing events in 119 murine transcripts in-vivo
date: 2021
words: nan
sentences: nan
pages:
flesch: nan
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id: 10_1101-2021_01_08_425885
author: Sritharan, Duluxan
title: Computing the Riemannian curvature of image patch and single-cell RNA sequencing data manifolds using extrinsic differential geometry
date: 2021
words: nan
sentences: nan
pages:
flesch: nan
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id: 10_1101-2021_01_04_425250
author: Thibodeau, Asa
title: A read count-based method to detect multiplets and their cellular origins from snATAC-seq data
date: 2021
words: 9658.0
sentences: 910.0
pages: 32
flesch: 59.0
cache: ./cache/10_1101-2021_01_04_425250.pdf
txt: ./txt/10_1101-2021_01_04_425250.txt
summary: A read count-based method to detect multiplets and their cellular origins from snATAC-seq data Similar to other droplet-based single cell assays, single nucleus ATAC-seq (snATAC-seq) data harbor multiplets 17 found that when snATAC-seq samples were adequately sequenced (e.g., >20k valid read pairs per cell), ATAC-52 ATAC-DoubletDetector detected heterotypic multiplets introduced in PBMC samples with high recall 126 detected multiplets were homotypic (76.7-84.3% in islets, 63-78.7% in PBMCs), with cell types being distributed 207 with respect to their cell proportions for both homotypic and heterotypic multiplet types (Fig. 5d-e, Extended Data 208 ATAC-DoubletDetector for identifying multiplets from snATAC-seq data with enough reads per nuclei, it can also 239 Fig. 2: ATAC-DoubletDetector identifies heterotypic and homotypic multiplets in human PBMC snATAC-seq data. e, The number of cells and percentage of multiplets detected by ATAC-DoubletDetector in PBMC and islet samples. Extended Data Fig. 6: ATAC-DoubletDetector detects both homotypic and heterotypic multiplets at high read depth.
id: 10_1101-2021_01_02_425006
author: Wang, Qi
title: Analysis of next- and third-generation RNA-Seq data reveals the structures of alternative transcription units in bacterial genomes
date: 2021
words: nan
sentences: nan
pages:
flesch: nan
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id: 10_1101-2021_01_06_425544
author: Wang, Xia
title: Complex Systems Analysis Informs on the Spread of COVID-19
date: 2021
words: nan
sentences: nan
pages:
flesch: nan
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id: 10_1101-2020_04_17_043323
author: Waschke, Johannes
title: linus: Conveniently explore, share, and present large-scale biological trajectory data from a web browser
date: 2021
words: 6528.0
sentences: 571.0
pages: 13
flesch: 54.0
cache: ./cache/10_1101-2020_04_17_043323.pdf
txt: ./txt/10_1101-2020_04_17_043323.txt
summary: linus: Conveniently explore, share, and present large-scale biological trajectory data from a web browser linus: Conveniently explore, share, and present large-scale biological trajectory data In biology, we are often confronted with information-rich, large-scale trajectory data, but exploring and communicating We provide a python script that reads trajectory data and enriches them with additional features, such as edge bundling or custom axes and generates an interactive web-based visualisation that can be shared offline from diffusion MRI imaging (Liu et al., 2020), or tracking data such as cell trajectories or animal trails (Romero-Ferrero et visualisation tool linus, making it easier to explore 3D trajectory data from any device without a local installation of Creating a visualisation package with linus is done in a few simple steps (Fig. 1a): The user imports trajectory data from a Figure 1 Browser-based exploration and sharing of trajectory visualizations with linus.
id: 10_1101-2021_01_04_425288
author: Wei, Qi
title: Predicting chemotherapy response using a variational autoencoder approach
date: 2021
words: nan
sentences: nan
pages:
flesch: nan
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id: 10_1101-2021_01_08_425379
author: Wilmes, Stephan
title: Competitive binding of STATs to receptor phospho-Tyr motifs accounts for altered cytokine responses in autoimmune disorders
date: 2021
words: nan
sentences: nan
pages:
flesch: nan
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id: 10_1101-2021_01_08_425855
author: Wu, Canbiao
title: DeepHBV: A deep learning model to predict hepatitis B virus (HBV) integration sites.
date: 2021
words: 7280.0
sentences: 806.0
pages: 31
flesch: 53.0
cache: ./cache/10_1101-2021_01_08_425855.pdf
txt: ./txt/10_1101-2021_01_08_425855.txt
summary: DeepHBV: A deep learning model to predict hepatitis B virus (HBV) integration sites. deep learning model DeepHBV to predict HBV integration sites by learning local learning model DeepHBV to predict HBV integration sites by learning local genomic DeepHBV effectively predicts HBV integration sites by adding genomic features. mixed HBV integration sequences, positive genome feature samples, and randomly peaks and DeepHBV with HBV integration sequences + TCGA Pan Cancer peaks) on model trained with HBV integrated sequences + TCGA Pan Cancer showed an performed better compared with DeepHBV model with HBV integration sequences + HBV integration sites + TCGA Pan Cancer, a cluster of attention weights much output of DeepHBV with HBV integration sites plus TCGA Pan Cancer showed the of DeepHBV with HBV integration sequences + TCGA Pan Cancer showed strong DeepHBV with HBV integration sequences + TCGA Pan Cancer model on (a) DeepHBV with HBV integration sequences + TCGA Pan Cancer model on (a)
id: 10_1101-2020_12_14_422697
author: Yang, Tao
title: AdRoit: an accurate and robust method to infer complex transcriptome composition
date: 2021
words: nan
sentences: nan
pages:
flesch: nan
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id: 10_1101-2021_01_08_425918
author: Yogodzinski, Christopher H
title: A global cancer data integrator reveals principles of synthetic lethality, sex disparity and immunotherapy.
date: 2021
words: nan
sentences: nan
pages:
flesch: nan
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id: 10_1101-2021_01_05_425417
author: Zielezinski, Andrzej
title: Taxonomy-aware, sequence similarity ranking reliably predicts phage-host relationships
date: 2021
words: nan
sentences: nan
pages:
flesch: nan
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==== make-pages.sh questions
==== make-pages.sh search
==== make-pages.sh topic modeling corpus
Zipping study carrel