{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,24]],"date-time":"2026-07-24T21:11:30Z","timestamp":1784927490452,"version":"3.55.0"},"reference-count":32,"publisher":"Oxford University Press (OUP)","issue":"17","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2016,9,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Motivation: Drug repositioning, which aims to identify new indications for existing drugs, offers a promising alternative to reduce the total time and cost of traditional drug development. Many computational strategies for drug repositioning have been proposed, which are based on similarities among drugs and diseases. Current studies typically use either only drug-related properties (e.g. chemical structures) or only disease-related properties (e.g. phenotypes) to calculate drug or disease similarity, respectively, while not taking into account the influence of known drug\u2013disease association information on the similarity measures.<\/jats:p>\n               <jats:p>Results: In this article, based on the assumption that similar drugs are normally associated with similar diseases and vice versa, we propose a novel computational method named MBiRW, which utilizes some comprehensive similarity measures and Bi-Random walk (BiRW) algorithm to identify potential novel indications for a given drug. By integrating drug or disease features information with known drug\u2013disease associations, the comprehensive similarity measures are firstly developed to calculate similarity for drugs and diseases. Then drug similarity network and disease similarity network are constructed, and they are incorporated into a heterogeneous network with known drug\u2013disease interactions. Based on the drug\u2013disease heterogeneous network, BiRW algorithm is adopted to predict novel potential drug\u2013disease associations. Computational experiment results from various datasets demonstrate that the proposed approach has reliable prediction performance and outperforms several recent computational drug repositioning approaches. Moreover, case studies of five selected drugs further confirm the superior performance of our method to discover potential indications for drugs practically.<\/jats:p>\n               <jats:p>Availability and Implementation: \u00a0http:\/\/github.com\/\/bioinfomaticsCSU\/MBiRW .<\/jats:p>\n               <jats:p>Contact: \u00a0jxwang@mail.csu.edu.cn<\/jats:p>\n               <jats:p>Supplementary information: \u00a0Supplementary data are available at Bioinformatics online.<\/jats:p>","DOI":"10.1093\/bioinformatics\/btw228","type":"journal-article","created":{"date-parts":[[2016,5,6]],"date-time":"2016-05-06T03:04:47Z","timestamp":1462503887000},"page":"2664-2671","source":"Crossref","is-referenced-by-count":447,"title":["Drug repositioning based on comprehensive similarity measures and Bi-Random walk algorithm"],"prefix":"10.1093","volume":"32","author":[{"given":"Huimin","family":"Luo","sequence":"first","affiliation":[{"name":"1 School of Information Science and Engineering, Central South University, ChangSha, 410083, China"},{"name":"2 School of Computer and Information Engineering, Henan University, KaiFeng 475001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianxin","family":"Wang","sequence":"additional","affiliation":[{"name":"1 School of Information Science and Engineering, Central South University, ChangSha, 410083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Min","family":"Li","sequence":"additional","affiliation":[{"name":"1 School of Information Science and Engineering, Central South University, ChangSha, 410083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Junwei","family":"Luo","sequence":"additional","affiliation":[{"name":"1 School of Information Science and Engineering, Central South University, ChangSha, 410083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoqing","family":"Peng","sequence":"additional","affiliation":[{"name":"1 School of Information Science and Engineering, Central South University, ChangSha, 410083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fang-Xiang","family":"Wu","sequence":"additional","affiliation":[{"name":"3 Division of Biomedical Engineering, University of Saskatchewan, Saskatchewan S7N 5A9, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi","family":"Pan","sequence":"additional","affiliation":[{"name":"4 Department of Computer Science, Georgia State University, Atlanta, GA 30302-3994, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2016,5,5]]},"reference":[{"key":"2023020112591295600_btw228-B1","doi-asserted-by":"crossref","first-page":"673","DOI":"10.1038\/nrd1468","article-title":"Drug repositioning: identifying and developing new uses for existing drugs","volume":"3","author":"Ashburn","year":"2004","journal-title":"Nat. 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