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dc.contributor.author최선-
dc.date.accessioned2016-08-28T12:08:17Z-
dc.date.available2016-08-28T12:08:17Z-
dc.date.issued2010-
dc.identifier.issn1093-3263-
dc.identifier.otherOAK-6599-
dc.identifier.urihttp://dspace.ewha.ac.kr/handle/2015.oak/220809-
dc.description.abstractAdenosine receptors (ARs) belong to the G-protein-coupled receptor (GPCR) superfamily and consist of four subtypes referred to as A1, A2A, A2B, and A3. It is important to develop potent and selective modulators of ARs for therapeutic applications. In order to develop reliable in silico models that can effectively classify antagonists of each AR, we carried out three machine learning methods: Laplacian-modified naïve Bayesian, recursive partitioning, and support vector machine. The results for each classification model showed values high in accuracy, sensitivity, specificity, area under the receiver operating characteristic curve and Matthews correlation coefficient. By highlighting representative antagonists, the models demonstrated their power and usefulness, and these models could be utilized to predict potential AR antagonists in drug discovery. © 2010 Elsevier Inc. All rights reserved.-
dc.languageEnglish-
dc.titleIn silico classification of adenosine receptor antagonists using Laplacian-modified naïve Bayesian, support vector machine, and recursive partitioning-
dc.typeArticle-
dc.relation.issue8-
dc.relation.volume28-
dc.relation.indexSCIE-
dc.relation.indexSCOPUS-
dc.relation.startpage883-
dc.relation.lastpage890-
dc.relation.journaltitleJournal of Molecular Graphics and Modelling-
dc.identifier.doi10.1016/j.jmgm.2010.03.008-
dc.identifier.wosidWOS:000278414800021-
dc.identifier.scopusid2-s2.0-77953547967-
dc.author.googleLee J.H.-
dc.author.googleLee S.-
dc.author.googleChoi S.-
dc.contributor.scopusid최선(8659831000)-
dc.date.modifydate20170508134458-
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약학대학 > 약학과 > Journal papers
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