Research Article: Kernel-based data fusion improves the drug-protein interaction prediction
Computational Biology and Chemistry
Similarity boosting for label noise tolerance in protein-chemical interaction prediction
Proceedings of the 2nd ACM Conference on Bioinformatics, Computational Biology and Biomedicine
Globalized bipartite local model for drug-target interaction prediction
Proceedings of the 11th International Workshop on Data Mining in Bioinformatics
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Drug-target interaction prediction for drug repurposing with probabilistic similarity logic
Proceedings of the 12th International Workshop on Data Mining in Bioinformatics
Predicting therapeutic targets with integration of heterogeneous data sources
PRIB'13 Proceedings of the 8th IAPR international conference on Pattern Recognition in Bioinformatics
Functional characterization of drug-protein interactions network
Intelligent Data Analysis
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Motivation: The identification of interactions between drugs and target proteins is a key area in genomic drug discovery. Therefore, there is a strong incentive to develop new methods capable of detecting these potential drug–target interactions efficiently. Results: In this article, we characterize four classes of drug–target interaction networks in humans involving enzymes, ion channels, G-protein-coupled receptors (GPCRs) and nuclear receptors, and reveal significant correlations between drug structure similarity, target sequence similarity and the drug–target interaction network topology. We then develop new statistical methods to predict unknown drug–target interaction networks from chemical structure and genomic sequence information simultaneously on a large scale. The originality of the proposed method lies in the formalization of the drug–target interaction inference as a supervised learning problem for a bipartite graph, the lack of need for 3D structure information of the target proteins, and in the integration of chemical and genomic spaces into a unified space that we call ‘pharmacological space’. In the results, we demonstrate the usefulness of our proposed method for the prediction of the four classes of drug–target interaction networks. Our comprehensively predicted drug–target interaction networks enable us to suggest many potential drug–target interactions and to increase research productivity toward genomic drug discovery. Availability: Softwares are available upon request. Contact: Yoshihiro.Yamanishi@ensmp.fr Supplementary information: Datasets and all prediction results are available at http://web.kuicr.kyoto-u.ac.jp/supp/yoshi/drugtarget/.