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.
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 ...
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 ...
When Hend Alqaderiwas studying how saliva could predict the risk of diabetes or the severity of a coronavirus infection, she collected a lot of saliva samples-thousands, measuring hundreds of bacteria ...
The partnership integrates high-resolution multi-omics data generation with predictive multimodal machine learning to support biopharma decision-making in neurology.
Approximately one in seven adults in the United States has kidney disease, where the organs responsible for filtering waste ...
Studying gene expression in a cancer patient's cells can help clinical biologists understand the cancer's origin and predict the success of different treatments. But cells are complex and contain many ...
Tiny RNA molecules carried by extracellular vesicles in the bloodstream can accurately predict kidney function decline and cardiovascular risk in ...
Building an AI infrastructure for biotech. Bacteria that munch on cancer. How to best make a cold brew. All that and more in ...
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