![]() ![]() Given the complexity and multifaceted nature of cardiovascular diseases in general, and CAD in particular, an approach that integrates all these factors into a risk-stratification model would be expected to better predict incident events than existent models. Machine learning (ML) and particularly deep learning (DL) algorithms are inherently designed to extract patterns and associations from large-scale data, including clinical and genomic data. Genome-wide association studies (GWASs) operate by simultaneous comparison of millions of SNPs between diseased individuals and disease-free controls to detect a statistically significant association between an SNP locus and a particular condition. This has paved the way for the emergence of modern data-driven sciences such as genomics and other “omics”. ![]() For instance, the completion of the Human Genome Project has paved the way to design single-nucleotide polymorphism (SNP) and mRNA microarrays, which can broadly test for millions of genetic variants in a simple point-of-care test. Over the last two decades, the emergence of technologies able to measure biological processes at a large scale have resulted in an enormous influx of data. Integration of Genetics and AI in Cardiovascular Diseases
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