GPCR-drug interactions prediction using random forest with drug-association-matrix-based post-processing procedure
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- 作者：Jun Hua ; Yang Lia ; Jing-Yu Yanga ; Hong-Bin Shenb ; hbshen@sjtu.edu.cn" class="auth_mail" title="E-mail the corresponding author ; Dong-Jun Yua ; c ; njyudj@njust.edu.cn" class="auth_mail" title="E-mail the corresponding author
- 关键词：GPCR&ndash ; drug interactions ; Random forest ; Drug association matrix ; Machine learning
- 刊名：Computational Biology and Chemistry
- 出版年：2016
- 出版时间：February 2016
- 年：2016
- 卷：60
- 期：Complete
- 页码：59-71
- 全文大小：1652 K
文摘
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The evolutionary feature of GPCR sequence and the wavelet-based molecular fingerprint feature of drug are integrated to form the discriminative feature for a GPCR–drug pair.
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A novel drug-association-matrix-based post-processing procedure is developed to reduce potential false positive or false negative of predictions.
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The implemented webserver, called TargetGDrug, is freely available for academic use at class="interref" data-locatorType="url" data-locatorKey="http://csbio.njust.edu.cn/bioinf/TargetGDrug">http://csbio.njust.edu.cn/bioinf/TargetGDrug.