TY - CHAP
T1 - Chemogenomic approaches to infer drug-target interaction networks
AU - Yamanishi, Yoshihiro
PY - 2013
Y1 - 2013
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84871893054&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84871893054&partnerID=8YFLogxK
U2 - 10.1007/978-1-62703-107-3-9
DO - 10.1007/978-1-62703-107-3-9
M3 - Chapter
C2 - 23192544
AN - SCOPUS:84871893054
SN - 9781627031066
VL - 939
T3 - Methods in Molecular Biology
SP - 97
EP - 113
BT - Data Mining for Systems Biology: Methods and Protocols
ER -