{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,9]],"date-time":"2026-01-09T01:59:11Z","timestamp":1767923951467,"version":"3.49.0"},"reference-count":22,"publisher":"Oxford University Press (OUP)","issue":"16","license":[{"start":{"date-parts":[[2016,10,2]],"date-time":"2016-10-02T00:00:00Z","timestamp":1475366400000},"content-version":"vor","delay-in-days":1222,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/3.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2013,8,15]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Motivation: The identification of drug\u2013target interaction (DTI) represents a costly and time-consuming step in drug discovery and design. Computational methods capable of predicting reliable DTI play an important role in the field. Recently, recommendation methods relying on network-based inference (NBI) have been proposed. However, such approaches implement naive topology-based inference and do not take into account important features within the drug\u2013target domain.<\/jats:p>\n               <jats:p>Results: In this article, we present a new NBI method, called domain tuned-hybrid (DT-Hybrid), which extends a well-established recommendation technique by domain-based knowledge including drug and target similarity. DT-Hybrid has been extensively tested using the last version of an experimentally validated DTI database obtained from DrugBank. Comparison with other recently proposed NBI methods clearly shows that DT-Hybrid is capable of predicting more reliable DTIs.<\/jats:p>\n               <jats:p>Availability: DT-Hybrid has been developed in R and it is available, along with all the results on the predictions, through an R package at the following URL: http:\/\/sites.google.com\/site\/ehybridalgo\/.<\/jats:p>\n               <jats:p>Contact: \u00a0apulvirenti@dmi.unict.it<\/jats:p>\n               <jats:p>Supplementary information: \u00a0Supplementary data are available at Bioinformatics online.<\/jats:p>","DOI":"10.1093\/bioinformatics\/btt307","type":"journal-article","created":{"date-parts":[[2013,5,30]],"date-time":"2013-05-30T00:18:49Z","timestamp":1369873129000},"page":"2004-2008","source":"Crossref","is-referenced-by-count":152,"title":["Drug\u2013target interaction prediction through domain-tuned network-based inference"],"prefix":"10.1093","volume":"29","author":[{"given":"Salvatore","family":"Alaimo","sequence":"first","affiliation":[{"name":"1 Department of Mathematics and Computer Science and 2Department of Clinical and Molecular Biomedicine, University of Catania, Catania, Italy"}]},{"given":"Alfredo","family":"Pulvirenti","sequence":"additional","affiliation":[{"name":"1 Department of Mathematics and Computer Science and 2Department of Clinical and Molecular Biomedicine, University of Catania, Catania, Italy"}]},{"given":"Rosalba","family":"Giugno","sequence":"additional","affiliation":[{"name":"1 Department of Mathematics and Computer Science and 2Department of Clinical and Molecular Biomedicine, University of Catania, Catania, Italy"}]},{"given":"Alfredo","family":"Ferro","sequence":"additional","affiliation":[{"name":"1 Department of Mathematics and Computer Science and 2Department of Clinical and Molecular Biomedicine, University of Catania, Catania, Italy"}]}],"member":"286","published-online":{"date-parts":[[2013,5,29]]},"reference":[{"key":"2023020305203170700_btt307-B1","doi-asserted-by":"crossref","first-page":"403","DOI":"10.1016\/S0022-2836(05)80360-2","article-title":"Basic local alignment search tool","volume":"215","author":"Altschul","year":"1990","journal-title":"J. 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