In silico models to predict which tumors will respond to a given drug are necessary for
Precision Oncology. However, predictive models are only available for a handful of cases …

Background: Machine learning models that rely on single omics data for drug sensitivity
prediction are challenging and frequently fail within precision medicine scenarios …

Background: Cancer cell lines are frequently used in research as in-vitro tumor models.
Genomic data and large-scale drug screening have accelerated the right drug selection for …

Motivation Anti-cancer drug response prediction is a central problem within stratified
medicine. Transcriptomic profiles of cancer cell lines are typically used for drug response …

Patient-derived xenografts (PDX) are a powerful and commonly used tool in preclinical drug
development as they represent an in vivo platform for screening drug response in human …

Background Machine learning has been utilized to predict cancer drug response from multi-
omics data generated from sensitivities of cancer cell lines to different therapeutic …

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 molecular analysis of tumors using highly sophisticated gene expression and
sequencing methods enables the comprehensive identification of drug targets and …

Motivation The increasing number of publicly available databases containing drugs'
chemical structures, their response in cell lines, and molecular profiles of the cell lines has …

A central goal of precision oncology is to administer an optimal drug treatment to each
cancer patient. A common preclinical approach to tackle this problem has been to …