Recent Advances in the Machine Learning-Based Drug-Target Interaction Prediction
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Published:2019-05-22
Issue:3
Volume:20
Page:194-202
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ISSN:1389-2002
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Container-title:Current Drug Metabolism
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language:en
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Short-container-title:CDM
Author:
Zhang Wen1, Lin Weiran1, Zhang Ding1, Wang Siman1, Shi Jingwen2, Niu Yanqing3
Affiliation:
1. School of Computer Science, Wuhan University, Wuhan 430072, China 2. School of Mathematics and Statistics, Wuhan University, Wuhan 430072, China 3. School of Mathematics and Statistics, South-Central University for Nationalities, Wuhan 430074, China
Abstract
Background:The identification of drug-target interactions is a crucial issue in drug discovery. In recent years, researchers have made great efforts on the drug-target interaction predictions, and developed databases, software and computational methods.Results:In the paper, we review the recent advances in machine learning-based drug-target interaction prediction. First, we briefly introduce the datasets and data, and summarize features for drugs and targets which can be extracted from different data. Since drug-drug similarity and target-target similarity are important for many machine learning prediction models, we introduce how to calculate similarities based on data or features. Different machine learningbased drug-target interaction prediction methods can be proposed by using different features or information. Thus, we summarize, analyze and compare different machine learning-based prediction methods.Conclusion:This study provides the guide to the development of computational methods for the drug-target interaction prediction.
Funder
Fundamental Research Funds for the Central Universities National Natural Science Foundation of China
Publisher
Bentham Science Publishers Ltd.
Subject
Clinical Biochemistry,Pharmacology
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