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 …

Artificial Intelligence is providing astonishing results, with medicine being one of its favourite
playgrounds. Machine Learning and, in particular, Deep Neural Networks are behind this …

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 …

Drug combination discovery depends on reliable synergy metrics but no consensus exists
on the correct synergy criterion to characterize combined interactions. The fragmented state …

Drug combinations are frequently used for the treatment of cancer patients in order to
increase efficacy, decrease adverse side effects, or overcome drug resistance. Given the …

Personalized cancer treatments using combinations of drugs with a synergistic effect is
attractive but proves to be highly challenging. Here we present an approach to uncover the …

Combination therapies are commonly used to treat patients with complex diseases that
respond poorly to single-agent therapies. In vitro high-throughput drug screening is a …

Stakeholders across the entire healthcare chain are looking to incorporate artificial
intelligence (AI) into their decision-making process. From early-stage drug discovery to …

Drug combinations are frequently used to treat cancer to reduce side effects and increase
efficacy. The experimental discovery of drug combination synergy is time-consuming and …