Interpretable machine learning leverages proteomics to improve cardiovascular disease risk prediction and biomarker identification.
Journal:
Communications medicine
Published Date:
May 19, 2025
Abstract
BACKGROUND: Cardiovascular diseases (CVDs) rank amongst the leading causes of long-term disability and mortality. Predicting CVD risk and identifying associated genes are crucial for prevention, early intervention, and drug discovery. The recent availability of UK Biobank Proteomics data enables investigation of blood proteins and their association with a variety of diseases. We sought to predict 10 year CVD risk using this data modality and known CVD risk factors.
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