Cancer is a leading cause of death worldwide. Identifying the best treatment using
computational models to personalize drug response prediction holds great promise to …

In-depth modeling of the complex interplay among multiple omics data measured from
cancer cell lines or patient tumors is providing new opportunities toward identification of …

Cell line drug screening datasets can be utilized for a range of different drug discovery
applications from drug biomarker discovery to building translational models of drug …

Inter and intra-tumoral heterogeneity are major stumbling blocks in the treatment of cancer
and are responsible for imparting differential drug responses in cancer patients. Recently …

Patient-derived tumor organoids (PDOs) are a highly promising preclinical model that
recapitulates the histology, gene expression, and drug response of the donor patient tumor …

Cancer drug development has been riddled with high attrition rates, in part, due to poor
reproducibility of preclinical models for drug discovery. Poor experimental design and lack of …

The identification of therapeutic biomarkers predictive of drug response is crucial in
personalized medicine. A number of computational models to predict response of anti …

Outcomes of anticancer therapy vary dramatically among patients due to diverse genetic
and molecular backgrounds, highlighting extensive intertumoral heterogeneity. The …

Cancer claims millions of lives yearly worldwide. While many therapies have been made
available in recent years, by in large cancer remains unsolved. Exploiting computational …

Resistance to therapy remains a major cause of cancer treatment failures, resulting in many
cancer-related deaths. Resistance can occur at any time during the treatment, even at the …