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

This Perspective explores the application of machine learning toward improved diagnosis
and treatment. We outline a vision for how machine learning can transform three broad …

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

Background Precise prediction of cancer types is vital for cancer diagnosis and therapy.
Through a predictive model, important cancer marker genes can be inferred. Several studies …

The emergence and continued reliance on the Internet and related technologies has
resulted in the generation of large amounts of data that can be made available for analyses …

Abstract Machine learning (ML) is a branch of artificial intelligence centered on algorithms
which do not need explicit prior programming to function but automatically learn from …

A major challenge in cancer treatment is predicting clinical response to anti-cancer drugs on
a personalized basis. Using a pharmacogenomics database of 1,001 cancer cell lines, we …

Artificial intelligence and machine learning (ML) promise to transform cancer therapies by
accurately predicting the most appropriate therapies to treat individual patients. Here, we …

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 …

Abstract Knowledge of the underpinnings of cancer initiation, progression and metastasis
has increased exponentially in recent years. Advanced “omics” coupled with machine …