Discover how AI is transforming nutritional science by turning complex diet and omics data into predictive tools that reshape chronic disease prevention and personalized care.

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

Machine learning for health data science, fuelled by proliferation of data and reduced computational costs, has garnered ...

Objective Cardiovascular diseases (CVD) remain the leading cause of mortality globally, necessitating early risk ...

BIOPREVENT’ AI tool predicts transplant-related immune conflict and mortality risk using biomarkers, helping doctors ...

Yale researchers have developed a machine learning model, called Immunostruct, that can help scientists create more ...

Both approaches identified hemoglobin as one of the most significant predictors of CKD risk. Additional top-ranked features included blood urea, sodium levels, red blood cell count, potassium, and ...

A powerful artificial intelligence (AI) tool could give clinicians a head start in identifying life-threatening complications after stem cell and bone marrow transplants, according to new research ...

A mysterious RNA found in breast cancer led scientists to uncover an entire hidden class of cancer-specific RNAs across ...

Insulin resistance - when the body doesn't properly respond to insulin, a hormone that helps control blood glucose levels - ...

The partnership integrates high-resolution multi-omics data generation with predictive multimodal machine learning to support biopharma decision-making in neurology.