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An AI-Integrated Framework for Precision Genomics in Coronary Artery Disease Using Whole Exome and Phenotypic Data
2026-01-30
genetic and genomic medicine
Title + abstract only
View on medRxiv
Show abstract
Coronary artery disease (CAD) is a multifactorial condition influenced by genetic, phenotypic, and environmental factors. Traditional risk prediction models fall short in capturing the polygenic complexity of CAD, particularly in underrepresented populations. This study presents SIGMA (Scoring Importance of Genes specific to disease using Machine learning Algorithms), a novel AI-powered framework that enhances CAD risk prediction by integrating genomic and phenotypic data. Our approach leverages...
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