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Predicting GPCR-drug interactions with multi-view featurecombination and random forest


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Predicting GPCR-drug interactions with multi-view featurecombination and random forest
Liu Guanghui 12Hu Jun 1Yu Dongjun 1
1.School of Computer Science and Engineering,Nanjing University of Science and Technology,Nanjing 210094,China; 2.School of Information Engineering,Nanjing University of Finance and Economics,Nanjing 210046,China
coupled recptors G-protein-coupled receptors drugs multi-view features amino acid composition sequence features molecular fingerprint random forest
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 experiments on benchmark datasets over both cross-validation and independent validation tests demonstrate the feasibility and efficacy of the proposed method.


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Last Update: 2016-02-29