Interpretable AI model could offer new insights into why medicines cause certain side effects, helping to improve future drug safety predictions.

IMF researchers show that satellite data, especially nighttime lights combined with machine learning can reliably estimate ...

Background Early graft failure within 90 postoperative days is the leading cause of mortality after heart transplantation. Existing risk scores, based on linear regression, often struggle to capture ...

Scientists have created an AI model that forecasts moderate heat stress—a major precursor to coral bleaching—at sites along ...

Supervised learning algorithms like Random Forests, XGBoost, and LSTMs dominate crypto trading by predicting price directions ...

Recentive Analytics, Inc. v. Fox Corp., No. 23-2437 (Fed. Cir. 2025) – On April 18, 2025, the Federal Circuit upheld the district court’s dismissal of the case on the ground that the patents were ...

A new study published in the journal of Scientific Reports proposed a potential diagnostic tool by combining deep learning ...

RFX-Fuse (Random Forests X [X=compression] — Forest Unified Learning and Similarity Engine) delivers Breiman and Cutler's complete vision for Random Forests as a Forests Unified Machine Learning and ...

In a study titled Recent Applications of Machine Learning Algorithms for Pesticide Analysis in Food Samples, published in the ...

Scientists plotted 1 million data points to design a new map that provided a trove of details about the groundwater supply.

Background Suicide rates have increased over the last couple of decades globally, particularly in the United States and among populations with lower economic status who present at safety-net ...

Background: Heart failure (HF), with its distinct phenotypes, poses significant public health challenges. Early diagnosis of specific HF phenotypes is crucial for timely therapeutic intervention.