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A systematic approach to identify novel cancer drug targets using machine learning, inhibitor design and high-throughput screening.

Genome Med. 2014; 
JeonJouhyun,NimSatra,TeyraJoan,DattiAlessandro,WranaJeffrey L,SidhuSachdev S,MoffatJason,KimPhil
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Custom Vector Construction DNA fragments encoding the identified domains were synthesized (GenScript Inc., Piscataway, NJ, USA) and cloned into a vector designed for the expression and purification of domains fused to the carboxyl terminus of glutathione S-transferase (GST), as described previously [57]. Get A Quote

摘要

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 strong anti-proliferative effects of both synthetic peptide and small molecule inhibitors against our predicted targets.

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