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Data Mining for Systems Biology

Volume 939 of the series Methods in Molecular Biology pp 97-113

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Chemogenomic Approaches to Infer Drug–Target Interaction Networks

  • Yoshihiro YamanishiAffiliated withInstitut Curie, Centre de recherche Biologie du developpement, U900 Unit of Bioinformatics and Computational Systems Biology of Cancer Email author 

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Abstract

The identification of drug–target interactions from heterogeneous biological data is critical in the drug development. In this chapter, we review recently developed in silico chemogenomic approaches to infer unknown drug–target interactions from chemical information of drugs and genomic information of target proteins. We review several kernel-based statistical methods from two different viewpoints: binary classification and dimension reduction. In the results, we demonstrate the usefulness of the methods on the prediction of drug–target interactions from chemical structure data and genomic sequence data. We also discuss the characteristics of each method, and show some perspectives toward future research direction.

Key words

Drug–target interactions Compound–protein interactions Chemical genomics Genomic drug discovery Bipartite graph Supervised network inference