南京理工大学学报（自然科学版）2016，Vol.40Issue(1)：1-9,9.DOI:10.14177/j.cnki.32-1397n.2016.40.01.001
基于多视角特征组合与随机森林的G蛋白 偶联受体与药物相互作用预测
Predicting GPCR-drug interactions with multi-view feature combination and random forest
摘要
Abstract
In order to improve the accuracy of predicting the interactions between G-protein-coupled receptors ( GPCR ) and drugs, this paper develops a novel method based on multi-view feature combination and random forest for GPCR-Drug interactions prediction with high performance. In the method,GPCR features from amino acid composition and protein evolution views and drug feature from molecular fingerprint are extracted;the feature of every GPCR-Drug pair can be formulated by serially combining the multi-view features of GPCRs and drugs;the GPCR-Drug prediction model is constructed with the random forest algorithm under the developed feature representation. Stringent ex-periments on benchmark datasets over both cross-validation and independent validation tests demonstrate the feasibility and efficacy of the proposed method.关键词
偶联受体/G蛋白偶联受体/药物/多视角特征/氨基酸组分/序列特征/分子指纹/随机森林Key words
coupled recptors/G-protein-coupled receptors/drugs/multi-view features/amino acid composition/sequence features/molecular fingerprint/random forest分类
信息技术与安全科学引用本文复制引用
刘光徽,胡俊,於东军..基于多视角特征组合与随机森林的G蛋白 偶联受体与药物相互作用预测[J].南京理工大学学报（自然科学版）,2016,40(1):1-9,9.基金项目
国家自然科学基金( 61373062 ) （ 61373062 ）
江苏省自然科学基金( BK20141403 ) （ BK20141403 ）
江苏省"六大人才高峰"项目(2013-XXRJ-022) （2013-XXRJ-022）
