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It is a time-consuming task to detect drug\u2013target interactions (DTIs) through biochemical experiments. At present, machine learning (ML) has been widely applied in large-scale drug screening. However, there are few methods for multiple information fusion. We propose a multiple kernel-based triple collaborative matrix factorization (MK-TCMF) method to predict DTIs. The multiple kernel matrices (contain chemical, biological and clinical information) are integrated via multi-kernel learning (MKL) algorithm. And the original adjacency matrix of DTIs could be decomposed into three matrices, including the latent feature matrix of the drug space, latent feature matrix of the target space and the bi-projection matrix (used to join the two feature spaces). To obtain better prediction performance, MKL algorithm can regulate the weight of each kernel matrix according to the prediction error. The weights of drug side-effects and target sequence are the highest. Compared with other computational methods, our model has better performance on four test data sets.<\/jats:p>","DOI":"10.1093\/bib\/bbab582","type":"journal-article","created":{"date-parts":[[2021,12,20]],"date-time":"2021-12-20T12:09:59Z","timestamp":1640002199000},"source":"Crossref","is-referenced-by-count":74,"title":["Identification of drug\u2013target interactions via multiple kernel-based triple collaborative matrix factorization"],"prefix":"10.1093","volume":"23","author":[{"given":"Yijie","family":"Ding","sequence":"first","affiliation":[{"name":"Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, P.R.China"}]},{"given":"Jijun","family":"Tang","sequence":"additional","affiliation":[{"name":"Department of Computational Science and Engineering, University of South Carolina, Columbia, U.S"}]},{"given":"Fei","family":"Guo","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Central South University, Changsha, P.R.China"}]},{"given":"Quan","family":"Zou","sequence":"additional","affiliation":[{"name":"Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, P.R.China"}]}],"member":"286","published-online":{"date-parts":[[2022,2,2]]},"reference":[{"issue":"D1","key":"2022031506303123800_ref1","doi-asserted-by":"crossref","first-page":"1113","DOI":"10.1093\/nar\/gkr912","article-title":"SuperTarget goes quantitative: update on drug-target interactions","volume":"40","author":"Hecker","year":"2012","journal-title":"Nucleic Acids Res"},{"issue":"D1","key":"2022031506303123800_ref2","doi-asserted-by":"crossref","first-page":"764","DOI":"10.1093\/nar\/gks1049","article-title":"BRENDA in 2013: integrated reactions, kinetic data, enzyme function data, improved disease classification: new options and contents in BRENDA","volume":"41","author":"Schomburg","year":"2013","journal-title":"Nucleic Acids Res"},{"issue":"Suppl 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