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Chemogenomic approaches to infer drug-target interaction networks.
MedLine Citation:
PMID:  23192544     Owner:  NLM     Status:  In-Data-Review    
Abstract/OtherAbstract:
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.
Authors:
Yoshihiro Yamanishi
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Publication Detail:
Type:  Journal Article    
Journal Detail:
Title:  Methods in molecular biology (Clifton, N.J.)     Volume:  939     ISSN:  1940-6029     ISO Abbreviation:  Methods Mol. Biol.     Publication Date:  2013  
Date Detail:
Created Date:  2012-11-29     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  9214969     Medline TA:  Methods Mol Biol     Country:  United States    
Other Details:
Languages:  eng     Pagination:  97-113     Citation Subset:  IM    
Affiliation:
Institut Curie, Centre de recherche Biologie du developpement, U900 Unit of Bioinformatics and Computational Systems Biology of Cancer, Paris, France, yoshihiro.yamanishi@ensmp.fr.
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