Chapter title |
Chemogenomic approaches to infer drug-target interaction networks.
|
---|---|
Chapter number | 9 |
Book title |
Data Mining for Systems Biology
|
Published in |
Methods in molecular biology, December 2012
|
DOI | 10.1007/978-1-62703-107-3_9 |
Pubmed ID | |
Book ISBNs |
978-1-62703-106-6, 978-1-62703-107-3
|
Authors |
Yamanishi Y, Yoshihiro Yamanishi |
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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Unknown | 39 | 95% |
Demographic breakdown
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Student > Master | 6 | 15% |
Student > Ph. D. Student | 4 | 10% |
Student > Bachelor | 4 | 10% |
Lecturer > Senior Lecturer | 2 | 5% |
Professor | 2 | 5% |
Other | 10 | 24% |
Unknown | 13 | 32% |
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Business, Management and Accounting | 1 | 2% |
Other | 6 | 15% |
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