Chapter title |
Sparse Modeling to Analyze Drug–Target Interaction Networks
|
---|---|
Chapter number | 13 |
Book title |
Data Mining for Systems Biology
|
Published in |
Methods in molecular biology, January 2018
|
DOI | 10.1007/978-1-4939-8561-6_13 |
Pubmed ID | |
Book ISBNs |
978-1-4939-8560-9, 978-1-4939-8561-6
|
Authors |
Yoshihiro Yamanishi, Yamanishi, Yoshihiro |
Abstract |
Most drugs produce their phenotypic effects by interacting with target proteins, and understanding the molecular features that underpin drug-target interactions is crucial when designing a novel drug. In this chapter, we introduce the protocols that have driven recent advances in sparse modeling methods for analyzing drug-target interaction networks within a chemogenomic framework. In this approach, the chemical structures of candidate drug compounds are correlated with the genomic sequences of the candidate target proteins. We demonstrate the use of sparse canonical correspondence analysis and sparsity-induced binary classifiers to extract the underlying molecular features that are most strongly involved in drug-target interactions. We focus on drug chemical substructures and protein domains. Workflows for applying these methods are presented, and an application is described in detail. We consider the characteristics of each method and suggest possible directions for future research. |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 4 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 2 | 50% |
Student > Bachelor | 1 | 25% |
Unknown | 1 | 25% |
Readers by discipline | Count | As % |
---|---|---|
Biochemistry, Genetics and Molecular Biology | 2 | 50% |
Agricultural and Biological Sciences | 1 | 25% |
Unknown | 1 | 25% |