id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
10_1101-2020_02_04_934216	Kirchoff, Kathryn E.	EMBER: Multi-label prediction of kinase-substrate phosphorylation events through deep learning	2021	13	.pdf	application/pdf	8121	726	59	EMBER: Multi-label prediction of kinase-substrate phosphorylation events through deep learning task of kinase-motif phosphorylation prediction as a multi-label kinase or substrate, as well as protein scaffolds that facilitate structural orientation and downstream catalysis of the reaction, modify the efficacy of motif phosphorylation. prediction of phosphorylation events), a deep learning approach for predicting multi-label kinase-motif phosphorylation relationships. example, the TLK kinase family only has nine positive labels (verified TLK-motif interactions) and more than 10,000 resulting data set is comprised of 7302 phosphorylatable motifs and their reaction-associated kinase families (Table 1). The final output is a vector, k, of length eight, where each value corresponds to the probability that the motif a was phosphorylated by one of the kinase families indicated in We sought to illuminate the relationship between kinase-family dissimilarity and phosphorylated motif-group dissimilarity described results provide motivation to incorporate both motif dissimilarity and kinase relatedness into the predictive model, as of kinase-motif prediction compared to the single-label approaches.	./cache/10_1101-2020_02_04_934216.pdf	./txt/10_1101-2020_02_04_934216.txt