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Chemogenomic Approaches to Infer Drug–Target Interaction Networks

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Data Mining for Systems Biology

Part of the book series: Methods in Molecular Biology ((MIMB,volume 939))

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.

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Authors and Affiliations

  1. Institut Curie, Centre de recherche Biologie du developpement, U900 Unit of Bioinformatics and Computational Systems Biology of Cancer, Paris, France

    Yoshihiro Yamanishi

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  1. Yoshihiro Yamanishi
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Correspondence to Yoshihiro Yamanishi .

Editor information

Editors and Affiliations

  1. , Institute for Chemical Research, Kyoto University, Gokasho, Uji, Kyoto, 611-0011, Japan

    Hiroshi Mamitsuka

  2. Dept. Biomedical Engineering, Boston University, Cummington St. 44, Boston, 02215, Massachusetts, USA

    Charles DeLisi

  3. Inst. Chemical Research, Bioinformatics Center, Kyoto University, Gokasho, Uji, 611-0011, Kyoto, Japan

    Minoru Kanehisa

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Yamanishi, Y. (2013). Chemogenomic Approaches to Infer Drug–Target Interaction Networks. In: Mamitsuka, H., DeLisi, C., Kanehisa, M. (eds) Data Mining for Systems Biology. Methods in Molecular Biology, vol 939. Humana Press, Totowa, NJ. https://doi.org/10.1007/978-1-62703-107-3_9

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  • DOI: https://doi.org/10.1007/978-1-62703-107-3_9

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  • Publisher Name: Humana Press, Totowa, NJ

  • Print ISBN: 978-1-62703-106-6

  • Online ISBN: 978-1-62703-107-3

  • eBook Packages: Springer Protocols

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