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 …

Combinatorial therapies have been recently proposed to improve the efficacy of anticancer
treatment. The SynergyFinder R package is a software used to analyze pre-clinical drug …

Only around 20% of the human proteome is considered to be druggable with small-molecule
antagonists. This leaves some of the most compelling therapeutic targets outside the reach …

Artificial intelligence transforms drug discovery, with phenotype-based approaches
emerging as a promising alternative to target-based methods, overcoming limitations like …

Combinatorial therapies that target multiple pathways have shown great promises for
treating complex diseases. DrugComb (https://drugcomb. org/) is a web-based portal for the …

Biomedical data is accumulating at a fast pace and integrating it into a unified framework is a
major challenge, so that multiple views of a given biological event can be considered …

Rapid proliferation is a hallmark of cancer associated with sensitivity to therapeutics that
cause DNA replication stress (RS). Many tumors exhibit drug resistance, however, via …

Predicting in vivo response to antineoplastics remains an elusive challenge. We performed
a first-of-kind evaluation of two transcriptome-based precision cancer medicine …

Motivation Drug combination therapy shows significant advantages over monotherapy in
cancer treatment. Since the combinational space is difficult to be traversed experimentally …

Due to their immunosuppressive role, tumor-infiltrating regulatory T cells (TI-Tregs)
represent attractive immuno-oncology targets. Analysis of TI vs. peripheral Tregs (P-Tregs) …