基于多视图矩阵补全的蛋白受体功能预测OA北大核心CSTPCD

Protein receptors are important component of cellular signal transduction and the most important drug targets in humans,with G Protein Coupled Receptors(GPCRs)accounting for the vast majority.GPCRs involve the most important drug targets in humans,accounting for about 34％of drugs on the market.Accurately annotating biological functions of GPCR proteins is vital to understand physiological processes involved and for targeted drug discovery,with Gene Ontology(GO)being the most commonly used way to describe protein function.Both GPCR proteins and GO contain multiple view information,and effectively utilizing this information improves protein function prediction performance.Therefore,this paper proposes a multi-view inductive matrix completion method MVIMC(Multi-View Inductive Matrix Completion)for predicting GO functions of GPCR proteins.MVIMC effectively utilizes GPCR protein and GO label view information,with GPCR containing textual and domain information,and GO containing textual information.Experimental results show that MVIMC achieves prediction probabilities of 68％and 69％for molecular function and biological process,respectively,which are better than the best current matrix completion methods and common methods in the CAFA protein function prediction competition.