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GPCR–drug interactions prediction using random forest with drug-association-matrix-based post-processing procedure

Paper ID Volume ID Publish Year Pages File Format Full-Text
14963 1363 2016 13 PDF Available
Title
GPCR–drug interactions prediction using random forest with drug-association-matrix-based post-processing procedure
Abstract

•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.•A novel drug-association-matrix-based post-processing procedure is developed to reduce potential false positive or false negative of predictions.•The implemented webserver, called TargetGDrug, is freely available for academic use at http://csbio.njust.edu.cn/bioinf/TargetGDrug.

G-protein-coupled receptors (GPCRs) are important targets of modern medicinal drugs. The accurate identification of interactions between GPCRs and drugs is of significant importance for both protein function annotations and drug discovery. In this paper, a new sequence-based predictor called TargetGDrug is designed and implemented for predicting GPCR–drug interactions. In TargetGDrug, the evolutionary feature of GPCR sequence and the wavelet-based molecular fingerprint feature of drug are integrated to form the combined feature of a GPCR–drug pair; then, the combined feature is fed to a trained random forest (RF) classifier to perform initial prediction; finally, a novel drug-association-matrix-based post-processing procedure is applied to reduce potential false positive or false negative of the initial prediction. Experimental results on benchmark datasets demonstrate the efficacy of the proposed method, and an improvement of 15% in the Matthews correlation coefficient (MCC) was observed over independent validation tests when compared with the most recently released sequence-based GPCR–drug interactions predictor. The implemented webserver, together with the datasets used in this study, is freely available for academic use at http://csbio.njust.edu.cn/bioinf/TargetGDrug.

Graphical abstractA new sequence-based predictor called TargetGDrug is designed and implemented for predicting GPCR–drug interactions. The evolutionary feature of GPCR sequence and the wavelet-based molecular fingerprint feature of drug are integrated to form the combined feature of a GPCR–drug pair; the combined feature is fed to a trained random forest classifier to perform initial prediction; finally, a novel drug-association-matrix-based post-processing procedure is applied to reduce potential false positive or false negative of the initial prediction. The webserver is freely available for academic use at http://csbio.njust.edu.cn/bioinf/TargetGDrug.Figure optionsDownload full-size imageDownload as PowerPoint slide

Keywords
GPCR–drug interactions; Random forest; Drug association matrix; Machine learning
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GPCR–drug interactions prediction using random forest with drug-association-matrix-based post-processing procedure
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Publisher
Database: Elsevier - ScienceDirect
Journal: Computational Biology and Chemistry - Volume 60, February 2016, Pages 59–71
Authors
, , , , ,
Subjects
Physical Sciences and Engineering Chemical Engineering Bioengineering
Get Full-Text Now
Don't Miss Today's Special Offer
Price was $35.95
You save - $31
Price after discount Only $4.95
100% Money Back Guarantee
Full-text PDF Download
Online Support
Any Questions? feel free to contact us