{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T12:27:40Z","timestamp":1781094460207,"version":"3.54.1"},"reference-count":18,"publisher":"Oxford University Press (OUP)","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2013,1,15]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Motivation: \u00a0In silico methods provide efficient ways to predict possible interactions between drugs and targets. Supervised learning approach, bipartite local model (BLM), has recently been shown to be effective in prediction of drug\u2013target interactions. However, for drug-candidate compounds or target-candidate proteins that currently have no known interactions available, its pure \u2018local\u2019 model is not able to be learned and hence BLM may fail to make correct prediction when involving such kind of new candidates.<\/jats:p>\n               <jats:p>Results: We present a simple procedure called neighbor-based interaction-profile inferring (NII) and integrate it into the existing BLM method to handle the new candidate problem. Specifically, the inferred interaction profile is treated as label information and is used for model learning of new candidates. This functionality is particularly important in practice to find targets for new drug-candidate compounds and identify targeting drugs for new target-candidate proteins. Consistent good performance of the new BLM\u2013NII approach has been observed in the experiment for the prediction of interactions between drugs and four categories of target proteins. Especially for nuclear receptors, BLM\u2013NII achieves the most significant improvement as this dataset contains many drugs\/targets with no interactions in the cross-validation. This demonstrates the effectiveness of the NII strategy and also shows the great potential of BLM\u2013NII for prediction of compound\u2013protein interactions.<\/jats:p>\n               <jats:p>Contact: \u00a0jpmei@ntu.edu.sg<\/jats:p>\n               <jats:p>Supplementary information: \u00a0Supplementary data are available at Bioinformatics online.<\/jats:p>","DOI":"10.1093\/bioinformatics\/bts670","type":"journal-article","created":{"date-parts":[[2012,11,18]],"date-time":"2012-11-18T02:50:02Z","timestamp":1353207002000},"page":"238-245","source":"Crossref","is-referenced-by-count":342,"title":["Drug\u2013target interaction prediction by learning from local information and neighbors"],"prefix":"10.1093","volume":"29","author":[{"given":"Jian-Ping","family":"Mei","sequence":"first","affiliation":[{"name":"1 Bioinformatics Research Centre, School of Computer Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore and 2Institute for Infocomm Research, A*Star, 1 Fusionopolis Way #21-01 Connexis, Singapore 138632, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chee-Keong","family":"Kwoh","sequence":"additional","affiliation":[{"name":"1 Bioinformatics Research Centre, School of Computer Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore and 2Institute for Infocomm Research, A*Star, 1 Fusionopolis Way #21-01 Connexis, Singapore 138632, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peng","family":"Yang","sequence":"additional","affiliation":[{"name":"1 Bioinformatics Research Centre, School of Computer Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore and 2Institute for Infocomm Research, A*Star, 1 Fusionopolis Way #21-01 Connexis, Singapore 138632, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiao-Li","family":"Li","sequence":"additional","affiliation":[{"name":"1 Bioinformatics Research Centre, School of Computer Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore and 2Institute for Infocomm Research, A*Star, 1 Fusionopolis Way #21-01 Connexis, Singapore 138632, Singapore"},{"name":"1 Bioinformatics Research Centre, School of Computer Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore and 2Institute for Infocomm Research, A*Star, 1 Fusionopolis Way #21-01 Connexis, Singapore 138632, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jie","family":"Zheng","sequence":"additional","affiliation":[{"name":"1 Bioinformatics Research Centre, School of Computer Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore and 2Institute for Infocomm Research, A*Star, 1 Fusionopolis Way #21-01 Connexis, Singapore 138632, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2012,11,17]]},"reference":[{"key":"2023012810164475000_bts670-B1","doi-asserted-by":"crossref","first-page":"2397","DOI":"10.1093\/bioinformatics\/btp433","article-title":"Supervised prediction of drug\u2013target interactions using bipartite local models","volume":"25","author":"Bleakley","year":"2009","journal-title":"Bioinformatics"},{"key":"2023012810164475000_bts670-B2","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1126\/science.1158140","article-title":"Drug target identification using side-effect similarity","volume":"321","author":"Campillos","year":"2008","journal-title":"Science"},{"key":"2023012810164475000_bts670-B3","doi-asserted-by":"crossref","first-page":"1970","DOI":"10.1039\/c2mb00002d","article-title":"Drug\u2013target interaction prediction by random walk on the heterogeneous network","volume":"8","author":"Chen","year":"2012","journal-title":"Mol. 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