Harnessing Clinically Integrated Genomics And Transcriptomics For Disease Biomarker Identification

This article explores a novel approach that combines traditional statistical analysis with cutting-edge AI and ML techniques to identify critical biomarkers for predictive engines in CVD patients. The integration of clinical and genomic data for predictive treatment within a diverse cardiovascular population was analyzed, uncovering 18 transcriptomic biomarkers that are highly significant in the CVD population. These biomarkers were used to predict disease with up to 96% accuracy.