Large-scale perturbation databases, such as Connectivity Map (CMap) or Library of
Integrated Network-based Cellular Signatures (LINCS), provide enormous opportunities for …

Predicting the sensitivity of tumors to specific anti-cancer treatments is a challenge of
paramount importance for precision medicine. Machine learning (ML) algorithms can be …

The mechanisms by which cells adapt to proteotoxic stress are largely unknown, but are key
to understanding how tumor cells, particularly in vivo, are largely resistant to proteasome …

Drug sensitivity and resistance are conventionally quantified by IC50 or E max values, but
these metrics are highly sensitive to the number of divisions taking place over the course of …

Abstract CellMiner Cross-Database (CellMinerCDB, discover. nci. nih. gov/cellminercdb)
allows integration and analysis of molecular and pharmacological data within and across …

Successful drug discovery is like finding oases of safety and efficacy in chemical and
biological deserts. Screens in disease models, and other decision tools used in drug …

Motivation Individualized drug response prediction is a fundamental part of personalized
medicine for cancer. Great effort has been made to discover biomarkers or to develop …

Drug combinations are of great interest for cancer treatment. Unfortunately, the discovery of
synergistic combinations by purely experimental means is only feasible on small sets of …

Motivation As we move toward an era of precision medicine, the ability to predict patient-
specific drug responses in cancer based on molecular information such as gene expression …

Recent cancer pharmacogenomic studies profiled large panels of cell lines against
hundreds of approved drugs and experimental chemical compounds. The overarching goal …