文献类型：期刊文献

英文题名：Integrated multi-similarity fusion and heterogeneous graph inference for drug-target interaction prediction

作者：Lian, Majun[1];Wang, Xinjie[1,2];Du, Wenli[1,2]

机构：[1]East China Univ Sci & Technol, Key Lab Smart Mfg Energy Chem Proc Minist Educ, Minist Educ, Shanghai 200237, Peoples R China;[2]East China Univ Sci & Technol, Sch Informat Sci & Engn, Shanghai 200237, Peoples R China

年份：2022

卷号：500

起止页码：1

外文期刊名：NEUROCOMPUTING

收录：;EI(收录号：20222312196371);WOS:【SCI-EXPANDED(收录号:WOS:000812309600001)】；

基金：This work was supported by the National Natural Science Foun-dation of China (Basic Science Center Program: 61988101) , National Natural Science Fund for Distinguished Young Scholars (61725301) and Fundamental Research Funds for the Central Universities (222202117006) .

语种：英文

外文关键词：Drug-target interactions prediction; Degree distribution; Multi-similarity fusion; Heterogeneous graph inference

摘要：Drug-target interaction (DTI) prediction performs a crucial part in drug discovery and design. Although many computational approaches for such prediction have been proposed, current researches still generally adopt chemical similarities of drugs or the sequence similarities of targets. However, the valuable information of known interactions has not been noticed, and the existing noise and useless information reduce the accuracy of DTI prediction. In addition, many existing computational approaches ignore the behavior information between nodes of the DTI network. In this paper, we develop an ensemble computational approach called integrated multi-similarity fusion and heterogeneous graph inference. First, based on the known DTI network, the degree distribution of drug and target similarities are analyzed and the noise and useless information are removed to improve prediction accuracy. Second, based on drug and target similarities and known DTIs, a strategy of multi-similarity fusion is proposed to capture potential useful information from known interactions that is used for enhancing drug and target similarities. Third, the heterogeneous graph inference is used to predict the DTIs to capture the edge weight (closeness) and behavior information (diffusion) between nodes of a heterogeneous network. To assist the reproducibility of our work and its comparison to published results, we perform experiments on four benchmark datasets. Results show that our approach outperforms some existing approaches and can contribute to predicting potential DTIs.(c) 2022 Elsevier B.V. All rights reserved.