A systematic approach to identify novel cancer drug targets using machine learning, inhibitor design and high-throughput screening.
Ontology highlight
ABSTRACT:
SUBMITTER: Jeon J
PROVIDER: S-EPMC4143549 | biostudies-literature | 2014
REPOSITORIES: biostudies-literature
Publications
A systematic approach to identify novel cancer drug targets using machine learning, inhibitor design and high-throughput screening.
Jeon Jouhyun J Nim Satra S Teyra Joan J Datti Alessandro A Wrana Jeffrey L JL Sidhu Sachdev S SS Moffat Jason J Kim Philip M PM
Genome medicine 20140730 7
We present an integrated approach that predicts and validates novel anti-cancer drug targets. We first built a classifier that integrates a variety of genomic and systematic datasets to prioritize drug targets specific for breast, pancreatic and ovarian cancer. We then devised strategies to inhibit these anti-cancer drug targets and selected a set of targets that are amenable to inhibition by small molecules, antibodies and synthetic peptides. We validated the predicted drug targets by showing s ...[more]
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