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.
Researchers developed and validated a machine-learning algorithm for predicting nutritional risk in patients with nasopharyngeal carcinoma.
In food drying applications, machine learning has demonstrated strong capability in predicting drying rates, moisture ...
Scholars analyze how the use of machine learning could reshape EPA drinking water standards.
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 ...
A new study published in the International Journal of General Medicine showed that physicians may reliably estimate the ...
Antimicrobial resistance (AMR) is an increasingly dangerous problem affecting global health. In 2019 alone, ...
11don MSN
A urine-based biological aging clock: Machine learning and microRNA offer accurate prediction
Craif Inc. in Nagoya, Japan, working with Nagoya University's Institute of Innovation for Future Society, has developed a ...
A scoping review shows machine learning models may help predict response to biologic and targeted synthetic DMARDs in ...
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