Researchers in the Nanoscience Center at the University of Jyväskylä, Finland, have developed a pioneering computational model that could expedite the use of nanomaterials in biomedical applications.

Scholars analyze how the use of machine learning could reshape EPA drinking water standards.

Researchers developed and validated a machine-learning algorithm for predicting nutritional risk in patients with nasopharyngeal carcinoma.

Jessica Lin and Zhenqi (Pete) Shi from Genentech describe a novel machine learning approach to predicting retention times for ...

NITK develops SVALSA, a machine learning-based landslide warning system for the Western Ghats, enhancing disaster ...

There is more than one way to describe a water molecule, especially when communicating with a machine learning (ML) model, says chemist Robert DiStasio. You can feed the algorithm the molecule's ...

Antimicrobial resistance (AMR) is an increasingly dangerous problem affecting global health. In 2019 alone, ...

Craif Inc. in Nagoya, Japan, working with Nagoya University's Institute of Innovation for Future Society, has developed a ...

An AI model using deep transfer learning—the most advanced form of machine learning—has predicted spoken language outcomes ...

A scoping review shows machine learning models may help predict response to biologic and targeted synthetic DMARDs in ...