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Kernel-based data fusion improves the drug-protein interaction prediction
Wang, Yong-Cui2; Zhang, Chun-Hua3; Deng, Nai-Yang4; Wang, Yong1
2011-12-14
Source PublicationCOMPUTATIONAL BIOLOGY AND CHEMISTRY
ISSN1476-9271
Volume35Issue:6Pages:353-362
AbstractProteins are involved in almost every action of every organism by interacting with other small molecules including drugs. Computationally predicting the drug-protein interactions is particularly important in speeding up the process of developing novel drugs. To borrow the information from existing drug-protein interactions, we need to define the similarity among proteins and the similarity among drugs. Usually these similarities are defined based on one single data source and many methods have been proposed. However, the availability of many genomic and chemogenomic data sources allows us to integrate these useful data sources to improve the predictions. Thus a great challenge is how to integrate these heterogeneous data sources. Here, we propose a kernel-based method to predict drug-protein interactions by integrating multiple types of data. Specially, we collect drug pharmacological and therapeutic effects, drug chemical structures, and protein genomic information to characterize the drug-target interactions, then integrate them by a kernel function within a support vector machine (SVM)-based predictor. With this data fusion technology, we establish the drug-protein interactions from a collections of data sources. Our new method is validated on four classes of drug target proteins, including enzymes, ion channels (ICs), G-protein couple receptors (GPCRs), and nuclear receptors (NRs). We find that every single data source is predictive and integration of different data sources allows the improvement of accuracy, i.e., data integration can uncover more experimentally observed drug-target interactions upon the same levels of false positive rate than single data source based methods. The functional annotation analysis indicates that our new predictions are worthy of future experimental validation. In conclusion, our new method can efficiently integrate diverse data sources, and will promote the further research in drug discovery. (C) 2011 Elsevier Ltd. All rights reserved.
KeywordDrug-target interaction Chemical space Pharmacological space Therapeutic space Genomic space Kernel function Support vector machine
DOI10.1016/j.compbiolchem.2011.10.003
Language英语
Funding ProjectNational Natural Science Foundation of China[10801131] ; National Natural Science Foundation of China[10971223] ; National Natural Science Foundation of China[11071252] ; SRF for ROCS, SEM ; Shanghai Key Laboratory of Intelligent Information Processing[IIPL-2010-008]
WOS Research AreaLife Sciences & Biomedicine - Other Topics ; Computer Science
WOS SubjectBiology ; Computer Science, Interdisciplinary Applications
WOS IDWOS:000298274400005
PublisherELSEVIER SCI LTD
Citation statistics
Cited Times:42[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.amss.ac.cn/handle/2S8OKBNM/12426
Collection应用数学研究所
Affiliation1.Chinese Acad Sci, Natl Ctr Math & Interdisciplinary Sci, Acad Math & Syst Sci, Beijing 100190, Peoples R China
2.Chinese Acad Sci, Key Lab Adaptat & Evolut Plateau Biota, NW Inst Plateau Biol, Xining 810001, Peoples R China
3.Renmin Univ China, Informat Sch, Beijing 100872, Peoples R China
4.China Agr Univ, Coll Sci, Beijing 100083, Peoples R China
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Wang, Yong-Cui,Zhang, Chun-Hua,Deng, Nai-Yang,et al. Kernel-based data fusion improves the drug-protein interaction prediction[J]. COMPUTATIONAL BIOLOGY AND CHEMISTRY,2011,35(6):353-362.
APA Wang, Yong-Cui,Zhang, Chun-Hua,Deng, Nai-Yang,&Wang, Yong.(2011).Kernel-based data fusion improves the drug-protein interaction prediction.COMPUTATIONAL BIOLOGY AND CHEMISTRY,35(6),353-362.
MLA Wang, Yong-Cui,et al."Kernel-based data fusion improves the drug-protein interaction prediction".COMPUTATIONAL BIOLOGY AND CHEMISTRY 35.6(2011):353-362.
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