We recently described a methodology that reliably predicted chemotherapeutic response in
multiple independent clinical trials. The method worked by building statistical models from …

The computational prediction of drug responses based on the analysis of multiple types of
genome-wide molecular data is vital for accomplishing the promise of precision medicine in …

Drug response prediction arises from both basic and clinical research of personalized
therapy, as well as drug discovery for cancers. With gene expression profiles and other …

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 …

Motivation: Panels of cell lines such as the NCI-60 have long been used to test drug
candidates for their ability to inhibit proliferation. Predictive models of in vitro drug sensitivity …

To enable personalized cancer treatment, machine learning models have been developed
to predict drug response as a function of tumor and drug features. However, most algorithm …

The goal of precision oncology is to tailor treatment for patients individually using the
genomic profile of their tumors. Pharmacogenomics datasets such as cancer cell lines are …

We demonstrate a method for the prediction of chemotherapeutic response in patients using
only before-treatment baseline tumor gene expression data. First, we fitted models for whole …

We consider the problem of predicting sensitivity of cancer cell lines to new drugs based on
supervised learning on genomic profiles. The genetic and epigenetic characterization of a …

Motivation Clinical response to anti-cancer drugs varies between patients. A large portion of
this variation can be explained by differences in molecular features, such as mutation status …