Systematic interrogation of diverse Omic data reveals interpretable, robust, and generalizable transcriptomic features of clinically successful therapeutic targets
Andrew D. Rouillard, Mark R. Hurle, Pankaj Agarwal
The paper identifies transcriptomic features of therapeutic targets that predict clinical success using Omic data and machine learning.
PLoS Computational Biology · May 21, 2018Peer-reviewedBioinformatics