Computation Of Open-Access Data To Assist Drug Discovery

"A research team led by scientists at UC San Francisco has developed a computational method that could make the discovery of new drugs for cancer and other diseases more efficient by systematic analysis of massive amounts of open-access data.

The method enables scientists to bypass the usual experiments in biological specimens and to instead do computational analyses, using open-access data to match FDA-approved drugs and other existing compounds to the molecular fingerprints of diseases like cancer. First, to identify cancer gene signatures, the research team probed The Cancer Genome Atlas for data and compared available gene signatures for tumors with adjacent healthy tissue (from human tissue samples). They then searched the Library of Integrated Network-based Cellular Signatures L1000 data set to determine the effect compounds had on the gene expression of those signatures. Next, they mined the database ChEMBL for drug-efficacy data. Lastly, the team probed the Cancer Cell Line Encyclopedia to map cell lines in the other databases."