{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,11]],"date-time":"2026-04-11T02:29:22Z","timestamp":1775874562871,"version":"3.50.1"},"reference-count":33,"publisher":"Oxford University Press (OUP)","issue":"18","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2012,9,15]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Motivation: Identifying interactions between drug compounds and target proteins has a great practical importance in the drug discovery process for known diseases. Existing databases contain very few experimentally validated drug\u2013target interactions and formulating successful computational methods for predicting interactions remains challenging.<\/jats:p>\n               <jats:p>Results: In this study, we consider four different drug\u2013target interaction networks from humans involving enzymes, ion channels, G-protein-coupled receptors and nuclear receptors. We then propose a novel Bayesian formulation that combines dimensionality reduction, matrix factorization and binary classification for predicting drug\u2013target interaction networks using only chemical similarity between drug compounds and genomic similarity between target proteins. The novelty of our approach comes from the joint Bayesian formulation of projecting drug compounds and target proteins into a unified subspace using the similarities and estimating the interaction network in that subspace. We propose using a variational approximation in order to obtain an efficient inference scheme and give its detailed derivations. Finally, we demonstrate the performance of our proposed method in three different scenarios: (i) exploratory data analysis using low-dimensional projections, (ii) predicting interactions for the out-of-sample drug compounds and (iii) predicting unknown interactions of the given network.<\/jats:p>\n               <jats:p>Availability: Software and Supplementary Material are available at http:\/\/users.ics.aalto.fi\/gonen\/kbmf2k.<\/jats:p>\n               <jats:p>Contact: \u00a0mehmet.gonen@aalto.fi<\/jats:p>\n               <jats:p>Supplementary information: \u00a0Supplementary data are available at Bioinformatics online.<\/jats:p>","DOI":"10.1093\/bioinformatics\/bts360","type":"journal-article","created":{"date-parts":[[2012,6,24]],"date-time":"2012-06-24T00:56:51Z","timestamp":1340499411000},"page":"2304-2310","source":"Crossref","is-referenced-by-count":346,"title":["Predicting drug\u2013target interactions from chemical and genomic kernels using Bayesian matrix factorization"],"prefix":"10.1093","volume":"28","author":[{"given":"Mehmet","family":"G\u00f6nen","sequence":"first","affiliation":[{"name":"Helsinki Institute for Information Technology HIIT, Department of Information and Computer Science, Aalto University School of Science, FI-00076 Aalto, Espoo, Finland"}]}],"member":"286","published-online":{"date-parts":[[2012,6,23]]},"reference":[{"key":"2023012512592198200_B1","doi-asserted-by":"crossref","first-page":"669","DOI":"10.1080\/01621459.1993.10476321","article-title":"Bayesian analysis of binary and polychotomous response data","volume":"88","author":"Albert","year":"1993","journal-title":"J. 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