A signal-processing–based framework converts DNA sequences into numerical signals to identify protein-coding regions. By integrating spectral ...

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 ...

Please provide your email address to receive an email when new articles are posted on . Researchers are using machine learning to identify data-driven PCOS subtypes. Findings may lead to more precise ...

Objective This study reviewed the current state of machine learning (ML) research for the prediction of sports-related injuries. It aimed to chart the various approaches used and assess their efficacy ...

The ability to anticipate what comes next has long been a competitive advantage -- one that's increasingly within reach for developers and organizations alike, thanks to modern cloud-based machine ...

White paper discusses how BullFrog AI’s bfPREP™ embodies data harmonization, enabling biopharma organizations to convert noisy, document-heavy data into standardized, AI-ready datasetsGAITHERSBURG, Md ...

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

A recent study, “Picking Winners in Factorland: A Machine Learning Approach to Predicting Factor Returns,” set out to answer a critical question: Can machine learning techniques improve the prediction ...

Cardiovascular disease continues to be the leading cause of death worldwide. To save lives, constantly improving diagnostic ...

Metabolite data and AI combine to redefine how we measure aging and predict health spans. Study: Metabolomic age (MileAge) predicts health and life span: A comparison of multiple machine learning ...