南京大学学报（自然科学版）2024，Vol.60Issue(1)：1-11,11.DOI:10.13232/j.cnki.jnju.2024.01.001
基于多视图矩阵补全的蛋白受体功能预测
Predicting functions of protein receptors through multi-view matrix completion
摘要
Abstract
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.关键词
G蛋白偶联受体/基因本体/矩阵补全/多视图学习Key words
G Protein-Coupled Receptors(GPCRs)/Gene Ontology/inductive matrix completion/multi-view learning分类
计算机与自动化引用本文复制引用
黄玮翔,丁季,刘夏栩,殷勤,兰闯闯,吴建盛..基于多视图矩阵补全的蛋白受体功能预测[J].南京大学学报（自然科学版）,2024,60(1):1-11,11.基金项目
国家自然科学基金(61872198,61971216),江苏省科技厅基础研究计划(BK20201378) （61872198,61971216）
