A new machine learning model built using a simple and interpretable approach predicts in-hospital death in patients with acute liver failure and reveals top risk drivers.

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

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

Researchers created an AI-driven liquid biopsy that scans patterns in fragments of DNA circulating in the blood. The system detected early liver fibrosis and cirrhosis—conditions that often go ...

PanMETAI combines AI and NMR metabolomics to detect early-stage pancreatic cancer from a blood sample, achieving 93 percent ...

AI algorithms have demonstrated a sensitivity of approximately 91% in detecting early dental caries, compared to 84% for ...

Tiny particles bounce light around in a unique way, a property that researchers are using to detect pollutants in water and ...

More than half of transplant recipients in a large analysis developed chronic graft-versus-host disease, and 15% died from causes other than cancer relapse. Those numbers capture the uneasy truth of ...

Researchers at the Johns Hopkins Kimmel Cancer Center report that an artificial intelligence (AI)-based liquid biopsy test ...

Researchers at the Johns Hopkins Kimmel Cancer Center report that an artificial intelligence (AI)-based liquid biopsy test ...

A machine learning-driven eNose detects ovarian cancer in blood plasma with 97 % sensitivity and specificity, offering a promising biomarker-agnostic approach.