Basecamp Research’s antibiotic design and vaccine prediction AI models are now available through Anthropic’s Claude Science.

Executives are making significant investment decisions based on AI outcomes they cannot independently verify. A machine ...

Target built a generative AI system to improve marketing campaign forecasting by retrieving and ranking similar historical ...

The AI-based program AlphaFold predicts a protein's 3D structure with remarkable accuracy. However, it tends to reduce heterogeneous structures to a single dominant conformation, or shape, and ...

Abstract: A number of machine learning (ML) algorithm based small signal modeling of Gallium Nitride (GaN) High Electron Mobility Transistors (HEMTs) have been reported in literature. However, these ...

Background: Machine learning (ML) and deep learning (DL) show promise for fall risk prediction, but prior reviews focused mainly on real-time fall detection, in-hospital falls, or conventional ...

This study aims to develop and validate a deep learning model based on Cone Beam Computed Tomography (CBCT) radiomic features to achieve early detection of potential carotid atherosclerosis in ...

Reference: Perego S. & Bonati L. Data efficient machine learning potentials for modeling catalytic reactivity via active learning and enhanced sampling, npj Computational Materials 10, 291 (2024) doi: ...

Learn how to compare ML models using bootstrap resampling with a hands-on sklearn implementation. Experts watched the viral White House video about Iran — their conclusions are telling A bad news day ...

In the domain of solid oral dosage forms, dissolution testing is a critical quality control tool. Regulatory agencies such as the U.S. Food and Drug Administration (FDA) and the European Medicines ...

ABSTRACT: In real-world applications, datasets frequently contain outliers, which can hinder the generalization ability of machine learning models. Bayesian classifiers, a popular supervised learning ...

Abstract: Learning causal structures from observational data is critical for causal discovery and many machine learning tasks. Traditional constraint-based methods first adopt conditional independence ...