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Kernel-based data fusion improves the drug-protein interaction prediction

文献类型：期刊论文

作者Wang, Yong-Cui2; Zhang, Chun-Hua3; Deng, Nai-Yang4; Wang, Yong1
刊名COMPUTATIONAL BIOLOGY AND CHEMISTRY
出版日期2011-12-14
卷号35期号:6页码:353-362
ISSN号1476-9271
关键词Drug-target interaction Chemical space Pharmacological space Therapeutic space Genomic space Kernel function Support vector machine
DOI10.1016/j.compbiolchem.2011.10.003
英文摘要Proteins 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.
资助项目National 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研究方向Life Sciences & Biomedicine - Other Topics ; Computer Science
语种英语
出版者ELSEVIER SCI LTD
WOS记录号WOS:000298274400005
源URL[http://ir.amss.ac.cn/handle/2S8OKBNM/12425]  
专题应用数学研究所
通讯作者Wang, Yong
作者单位1.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
推荐引用方式
GB/T 7714		 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.

入库方式： OAI收割

来源：数学与系统科学研究院

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