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 ...

Artificial intelligence (AI) is a two-edged sword. While AI and machine learning (ML) models are powerful, they can be known to make egregious mistakes. For example, the integration of AI and ...

Machine learning enhances proteomics by optimizing peptide identification, structure prediction, and biomarker discovery.

This new article publication from Acta Pharmaceutica Sinica B, discusses establishment of interpretable cytotoxicity prediction models using machine learning analysis of transcriptome features.

The calculation and/or the measurement of the thermal conductivity of materials is a fundamental challenge in materials science, essential for developing technologies in energy management, electronics ...

A physics informed machine learning model predicts thermal conductivity from infrared images in milliseconds, enabling fast, ...

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 ...

Influence of MRIs performed in a 6-week interval on the histopathological detection of prostate cancer with different PIRADS classifications: A real-world data analysis.

Support vector machines improve classification by mapping inseparable signals into higher-dimensional spaces. Random forest models, through ensemble decision trees, increase robustness against ...

Machine learning is a multibillion-dollar business with seemingly endless potential, but it poses some risks. Here's how to avoid the most common machine learning mistakes. Machine learning technology ...