Machine learning is a powerful tool in computational biology, enabling the analysis of a wide range of biomedical data such as genomic sequences and biological imaging. But when researchers use ...

Further simulations show that machine learning models can automatically capture non-additive effects and multi-locus interactions without explicitly specifying interaction terms, thereby improving the ...

A fast and accurate surrogate model screens over 10,000 possible metal-oxide supports for a platinum nanocatalyst to prevent sintering under high temperatures. Metal nanoparticles catalyze reactions ...

The National Academies of Sciences, Engineering, and Medicine are private, nonprofit institutions that provide expert advice on some of the most pressing challenges facing the nation and world. Our ...

This guest essay reflects the views of Nirali Somia, a graduate student at Cold Spring Harbor Laboratory. It is part of a series of essays from current researchers at the Cold Spring Harbor Laboratory ...

The field of interpretability investigates what machine learning (ML) models are learning from training datasets, the causes and effects of changes within a model, and the justifications behind its ...

Deep machine learning has achieved remarkable success in various fields of artificial intelligence, but achieving both high interpretability and high efficiency simultaneously remains a critical ...

Mentalising brain signatures reveal distinct self/other neural patterns from adolescence and are altered in schizophrenia, suggesting candidate neuromarkers.

Space weather forecasting remains a major challenge in heliophysics, as geomagnetic storms continue to pose significant risks to satellite operations, power ...

Work you complete in the non-credit experience will transfer to the for-credit experience when you upgrade and pay tuition. See How It Works for details. A previous version of Machine Learning: Theory ...