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
Measuring Relatedness Between Scientific Entities in Annotation Datasets
Proceedings of the International Conference on Bioinformatics, Computational Biology and Biomedical Informatics
Functional characterization of drug-protein interactions network
Intelligent Data Analysis
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Motivation:In silico prediction of drug–target interactions from heterogeneous biological data is critical in the search for drugs for known diseases. This problem is currently being attacked from many different points of view, a strong indication of its current importance. Precisely, being able to predict new drug–target interactions with both high precision and accuracy is the holy grail, a fundamental requirement for in silico methods to be useful in a biological setting. This, however, remains extremely challenging due to, amongst other things, the rarity of known drug–target interactions. Results: We propose a novel supervised inference method to predict unknown drug–target interactions, represented as a bipartite graph. We use this method, known as bipartite local models to first predict target proteins of a given drug, then to predict drugs targeting a given protein. This gives two independent predictions for each putative drug–target interaction, which we show can be combined to give a definitive prediction for each interaction. We demonstrate the excellent performance of the proposed method in the prediction of four classes of drug–target interaction networks involving enzymes, ion channels, G protein-coupled receptors (GPCRs) and nuclear receptors in human. This enables us to suggest a number of new potential drug–target interactions. Availability: An implementation of the proposed algorithm is available upon request from the authors. Datasets and all prediction results are available at http://cbio.ensmp.fr/~yyamanishi/bipartitelocal/. Contact: kevbleakley@gmail.com Supplementary information:Supplementary data are available at Bioinformatics online.