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. © 2013 Springer Science+Business Media New York.
CITATION STYLE
Yamanishi, Y. (2013). Chemogenomic approaches to infer drug-target interaction networks. Methods in Molecular Biology. Humana Press Inc. https://doi.org/10.1007/978-1-62703-107-3_9
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