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

Paper ID Volume ID Publish Year Pages File Format Full-Text
15192 1389 2011 10 PDF Available
Title
Kernel-based data fusion improves the drug–protein interaction prediction
Abstract

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.

Graphical abstractWe develop a kernel-based method to genome-widely predict drug–protein interactions by integrating various data sources to understand the function of drugs in a cellular network.Figure optionsDownload full-size imageDownload as PowerPoint slideHighlights► We develop new method to computationally predict drug–protein interactions. ► Pharmacological, therapeutic, chemical, and genomic data are collected. ► We integrate diverse data sources with a support vector machine. ► Every single data source is predictive for drug–protein interaction. ► Kernel-based data integration further improves the predictive accuracy.

Keywords
Drug–target interaction; Chemical space; Pharmacological space; Therapeutic space; Genomic space; Kernel function; Support vector machine
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Publisher
Database: Elsevier - ScienceDirect
Journal: Computational Biology and Chemistry - Volume 35, Issue 6, 14 December 2011, Pages 353–362
Authors
, , , ,
Subjects
Physical Sciences and Engineering Chemical Engineering Bioengineering
Get Full-Text Now
Don't Miss Today's Special Offer
Price was $35.95
You save - $31
Price after discount Only $4.95
100% Money Back Guarantee
Full-text PDF Download
Online Support
Any Questions? feel free to contact us