Chemogenomic approaches to infer drug-target interaction networks

Yoshihiro Yamanishi

Research output: Chapter in Book/Report/Conference proceedingChapter

38 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationData Mining for Systems Biology
Subtitle of host publicationMethods and Protocols
PublisherHumana Press Inc.
Pages97-113
Number of pages17
ISBN (Print)9781627031066
DOIs
Publication statusPublished - 2013

Publication series

NameMethods in Molecular Biology
Volume939
ISSN (Print)1064-3745

All Science Journal Classification (ASJC) codes

  • Molecular Biology
  • Genetics

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