Deep learning is a subdiscipline of artificial intelligence that uses a machine learning
technique called artificial neural networks to extract patterns and make predictions from …

Over the last decade, the nanomedicine has experienced unprecedented development in
diagnosis and management of diseases. A number of nanomedicines have been approved …

AI is undergoing a paradigm shift with the rise of models (eg, BERT, DALL-E, GPT-3) that are
trained on broad data at scale and are adaptable to a wide range of downstream tasks. We …

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 …

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 …

Next-generation DNA sequencing technology has dramatically advanced clinical oncology
through the identification of therapeutic targets and molecular biomarkers, leading to the …

Inter and intra-tumoral heterogeneity are major stumbling blocks in the treatment of cancer
and are responsible for imparting differential drug responses in cancer patients. Recently …

Recent advances in multiplexed single‐cell transcriptomics experiments facilitate the high‐
throughput study of drug and genetic perturbations. However, an exhaustive exploration of …

Simple Summary Circulating tumor DNA is DNA released by the tumor into the bloodstream.
In breast cancer, it is used mainly in research or in clinical trials, but it will likely be used in …

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 …