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RFDT: A Rotation Forest-based Predictor for Predicting Drug-Target Interactions Using Drug Structure and Protein Sequence Information
Wang, L (Wang, Lei); You, ZH (You, Zhu-Hong); Chen, X (Chen, Xing); Yan, X (Yan, Xin); Liu, G (Liu, Gang); Zhang, W (Zhang, Wei)
2018
发表期刊CURRENT PROTEIN & PEPTIDE SCIENCE
ISSN1389-2037
卷号19期号:5页码:445-454
摘要

Background: Identification of interaction between drugs and target proteins plays an important role in discovering new drug candidates. However, through the experimental method to identify the drug-target interactions remain to be extremely time-consuming, expensive and challenging even nowadays. Therefore, it is urgent to develop new computational methods to predict potential drugtarget interactions (DTI). Methods: In this article, a novel computational model is developed for predicting potential drug-target interactions under the theory that each drug-target interaction pair can be represented by the structural properties from drugs and evolutionary information derived from proteins. Specifically, the protein sequences are encoded as Position-Specific Scoring Matrix (PSSM) descriptor which contains information of biological evolutionary and the drug molecules are encoded as fingerprint feature vector which represents the existence of certain functional groups or fragments. Results: Four benchmark datasets involving enzymes, ion channels, GPCRs and nuclear receptors, are independently used for establishing predictive models with Rotation Forest (RF) model. The proposed method achieved the prediction accuracy of 91.3%, 89.1%, 84.1% and 71.1% for four datasets respectively. In order to make our method more persuasive, we compared our classifier with the state-of-theart Support Vector Machine (SVM) classifier. We also compared the proposed method with other excellent methods. Conclusions: Experimental results demonstrate that the proposed method is effective in the prediction of DTI, and can provide assistance for new drug research and development.

关键词Target Interactions Position-specific Scoring Matrix Auto Covariance Rotation Forest Support Vector Machine Drug Substructure Fingerprint
DOI10.2174/1389203718666161114111656
收录类别SCI
WOS记录号WOS:000428417500003
引用统计
被引频次:5[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://ir.xjipc.cas.cn/handle/365002/5296
专题多语种信息技术研究室
通讯作者You, ZH (You, Zhu-Hong)
作者单位1.China Univ Min & Technol, Sch Comp Sci & Technol, Xuzhou 221116, Peoples R China
2.Zaozhuang Univ, Coll Informat Sci & Engn, Zaozhuang 277100, Peoples R China
3.Chinese Acad Sci, Xinjiang Tech Inst Phys & Chem, Urumqi 830011, Peoples R China
4.China Univ Min & Technol, Sch Informat & Control Engn, Xuzhou 221116, Peoples R China
5.Zaozhuang Univ, Sch Foreign Languages, Zaozhuang 277100, Peoples R China
6.Shenzhen Univ, Coll Comp Sci & Software Engn, Shenzhen 518060, Peoples R China
推荐引用方式
GB/T 7714
Wang, L ,You, ZH ,Chen, X ,et al. RFDT: A Rotation Forest-based Predictor for Predicting Drug-Target Interactions Using Drug Structure and Protein Sequence Information[J]. CURRENT PROTEIN & PEPTIDE SCIENCE,2018,19(5):445-454.
APA Wang, L ,You, ZH ,Chen, X ,Yan, X ,Liu, G ,&Zhang, W .(2018).RFDT: A Rotation Forest-based Predictor for Predicting Drug-Target Interactions Using Drug Structure and Protein Sequence Information.CURRENT PROTEIN & PEPTIDE SCIENCE,19(5),445-454.
MLA Wang, L ,et al."RFDT: A Rotation Forest-based Predictor for Predicting Drug-Target Interactions Using Drug Structure and Protein Sequence Information".CURRENT PROTEIN & PEPTIDE SCIENCE 19.5(2018):445-454.
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