Kernel-based data fusion improves the drug-protein interaction prediction
Wang, Yong-Cui ; Zhang, Chun-Hua ; Deng, Nai-Yang ; Wang, Yong
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.
Document Type期刊论文
Recommended Citation
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.
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.
MLA Wang, Yong-Cui,et al."Kernel-based data fusion improves the drug-protein interaction prediction".COMPUTATIONAL BIOLOGY AND CHEMISTRY (2011).
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Wang, Yong-Cui]'s Articles
[Zhang, Chun-Hua]'s Articles
[Deng, Nai-Yang]'s Articles
Baidu academic
Similar articles in Baidu academic
[Wang, Yong-Cui]'s Articles
[Zhang, Chun-Hua]'s Articles
[Deng, Nai-Yang]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Wang, Yong-Cui]'s Articles
[Zhang, Chun-Hua]'s Articles
[Deng, Nai-Yang]'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.