Artificial intelligence (AI) is rapidly resha** cancer research and personalized clinical
care. Availability of high-dimensionality datasets coupled with advances in high …

Artificial intelligence (AI) has experienced explosive growth in oncology and related
specialties in recent years. The improved expertise in data capture, the increased capacity …

Most drugs entering clinical trials fail, often related to an incomplete understanding of the
mechanisms governing drug response. Machine learning techniques hold immense promise …

Predicting the sensitivity of tumors to specific anti-cancer treatments is a challenge of
paramount importance for precision medicine. Machine learning (ML) algorithms can be …

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 …

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 …

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 …

The computational prediction of drug responses based on the analysis of multiple types of
genome-wide molecular data is vital for accomplishing the promise of precision medicine in …

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 …

Pharmaceutical industry and the art and science of drug development are sorely in need of
novel transformative technologies in the current age of digital health and artificial …