XJIPC OpenIR  > 多语种信息技术研究室
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
Source PublicationCURRENT PROTEIN & PEPTIDE SCIENCE
ISSN1389-2037
Volume19Issue:5Pages:445-454
Abstract

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.

KeywordTarget Interactions Position-specific Scoring Matrix Auto Covariance Rotation Forest Support Vector Machine Drug Substructure Fingerprint
DOI10.2174/1389203718666161114111656
Indexed BySCI
WOS IDWOS:000428417500003
Citation statistics
Cited Times:14[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.xjipc.cas.cn/handle/365002/5296
Collection多语种信息技术研究室
Corresponding AuthorYou, ZH (You, Zhu-Hong)
Affiliation1.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
Recommended Citation
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.
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Wang, L (Wang, Lei)]'s Articles
[You, ZH (You, Zhu-Hong)]'s Articles
[Chen, X (Chen, Xing)]'s Articles
Baidu academic
Similar articles in Baidu academic
[Wang, L (Wang, Lei)]'s Articles
[You, ZH (You, Zhu-Hong)]'s Articles
[Chen, X (Chen, Xing)]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Wang, L (Wang, Lei)]'s Articles
[You, ZH (You, Zhu-Hong)]'s Articles
[Chen, X (Chen, Xing)]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.