Disclosing incoherent sparse and low-rank patterns inside homologous GPCR tasks for better modelling of ligand bioactivities
作者机构：School of Geographic and Biologic InformationNanjing University of Posts and TelecommunicationsNanjing210023China Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu ProvinceNanjing University of Posts and TelecommunicationsNanjing210023China Department of StatisticsUniversity of WarwickCoventryCV47ALUK Modern Economics&Management CollegeJiangxi University of Finance and EconomicsNanchang330013China School of Telecommunication and Information EngineeringNanjing University of Posts and TelecommunicationsNanjing210023China Verimake ResearchNanjing Qujike Info-tech Co.Ltd.Nanjing210088China
出 版 物：《Frontiers of Computer Science》 (中国计算机科学前沿（英文版）)
年 卷 期：2022年第16卷第4期
页 面：91-102页
核心收录：
学科分类：081203[工学-计算机应用技术] 08[工学] 0835[工学-软件工程] 0812[工学-计算机科学与技术（可授工学、理学学位）]
基 金：supported in part by the National Natural Science Foundation of China(Grant Nos.61872198,61971216,81771478,81973512) the Basic Research Program of Science and Technology Department of Jiangsu Province(BK20201378) the Natural Science Foundation of the Higher Education Institutions of Jiangsu Province(18KJB416005) the Natural Science Foundation of Nanjing University of Posts and Telecommunications(NY218092)
主 题：G protein-coupled receptors(GPCRs) ligand bioactivities multi-task learning incoherent sparse and lowrank patterns
摘 要：There are many new and potential drug targets in G protein-coupled receptors(GPCRs)without sufficient ligand associations,and accurately predicting and interpreting ligand bioactivities is vital for screening and optimizing hit compounds targeting these *** efficiently address the lack of labeled training samples,we proposed a multi-task regression learning with incoherent sparse and low-rank patterns(MTR-ISLR)to model ligand bioactivities and identify their key substructures associated with these GPCRs *** is,MTR-ISLR intends to enhance the performance and interpretability of models under a small size of available training data by introducing homologous GPCR ***,the low-rank constraint term encourages to catch the underlying relationship among homologous GPCR tasks for greater model generalization,and the entry-wise sparse regularization term ensures to recognize essential discriminative substructures from each task for explanative *** examined MTR-ISLR on a set of 31 important human GPCRs datasets from 9 subfamilies,each with less than 400 ligand *** results show that MTR-ISLR reaches better performance when compared with traditional single-task learning,deep multi-task learning and multi-task learning with joint feature learning-based models on most cases,where MTR-ISLR obtains an average improvement of 7%in correlation coefficient(r2)and 12%in root mean square error(RMSE)against the runner-up *** MTR-ISLR web server appends freely all source codes and data for academic usages.^(1))
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