COMET, a novel machine learning ... using only proteomics data, EHR data, or both. These baseline models solely used omics cohort data without pre-training. The EHR-only baseline model showed ...

While ML models are powerful tools for predicting diabetes, their lack of interpretability presents a major challenge for clinical adoption. Healthcare professionals require AI models to not only be ...

AI-enabled ECG biological age (ECG-BA) improves disease risk classification beyond chronological age, enhancing early ...

Enzymes significantly speed up the chemical reactions that keep you alive. Researchers are using AI to create new ones to tackle modern challenges.

To develop a machine learning (ML) algorithm to predict survival probabilities for patients with epithelial ovarian cancer (EOC).Data were obtained from the SEER database for women diagnosed with EOC ...

proposed a graph machine learning model, namely TREE, based on the Transformer framework. With this novel Transformer-based architecture, TREE not only identifies the most influential omics data ...

The PWAS approach, designed to enhance our understanding of how genetic variations influence diseases, begins with machine learning ... using data from the UK Biobank. Covering 99 common diseases ...

The measurements of about 3,000 plasma proteins were integrated with clinical information to construct sparse prediction models for 218 rare and common diseases. These diseases were associated ...