Predicting protein and pathway associations for understudied dark kinases using pattern-constrained knowledge graph embedding.

ABSTRACT: The 534 protein kinases encoded in the human genome constitute a large druggable class of proteins that include both well-studied and understudied "dark" members. Accurate prediction of dark kinase functions is a major bioinformatics challenge. Here, we employ a graph mining approach that uses the evolutionary and functional context encoded in knowledge graphs (KGs) to predict protein and pathway associations for understudied kinases. We propose a new scalable graph embedding approach, RegPattern2Vec, which employs regular pattern constrained random walks to sample diverse aspects of node context within a KG flexibly. RegPattern2Vec learns functional representations of kinases, interacting partners, post-translational modifications, pathways, cellular localization, and chemical interaction

SUBMITTER: Salcedo MV 

PROVIDER: S-EPMC10590106 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature