Driven by high-throughput sequencing techniques, modern genomic and clinical studies are
in a strong need of integrative machine learning models for better use of vast volumes of …

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 …

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

Identifying new lead molecules to treat cancer requires more than a decade of dedicated
effort. Before selected drug candidates are used in the clinic, their anti-cancer activity is …

Background Understanding the phenotypic drug response on cancer cell lines plays a vital
role in anti-cancer drug discovery and re-purposing. The Genomics of Drug Sensitivity in …

Background Drug-target networks are receiving a lot of attention in late years, given its
relevance for pharmaceutical innovation and drug lead discovery. Different in silico …

High-throughput screening technologies have provided a large amount of drug sensitivity
data for a panel of cancer cell lines and hundreds of compounds. Computational …

An enduring challenge in personalized medicine lies in selecting a suitable drug for each
individual patient. Here we concentrate on predicting drug responses based on a cohort of …

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 …

Background Human cancer cell lines are used in research to study the biology of cancer and
to test cancer treatments. Recently there are already some large panels of several hundred …