A team has proposed an interpretable machine learning approach that predicts print time and filament use for FFF, potentially ...

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 ...

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 findings show that boosting algorithms, a class of machine learning models, consistently outperform traditional statistical methods, particularly for traits with well-defined genetic signals. In ...

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 ...

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 ...

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 ...

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 ...

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 ...