Accurate prediction of kinase-substrate networks using knowledge graphs.

ABSTRACT: Phosphorylation of specific substrates by protein kinases is a key control mechanism for vital cell-fate decisions and other cellular processes. However, discovering specific kinase-substrate relationships is time-consuming and often rather serendipitous. Computational predictions alleviate these challenges, but the current approaches suffer from limitations like restricted kinome coverage and inaccuracy. They also typically utilise only local features without reflecting broader interaction context. To address these limitations, we have developed an alternative predictive model. It uses statistical relational learning on top of phosphorylation networks interpreted as knowledge graphs, a simple yet robust model for representing networked knowledge. Compared to a representative selection of

SUBMITTER: Novacek V 

PROVIDER: S-EPMC7738173 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature