ArticleJournal of the American Heart Association2023
Evolutionary Action-Machine Learning Model Identifies Candidate Genes Associated With Early-Onset Coronary Artery Disease.
Article in Journal of the American Heart Association, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it, 9 citations in OpenAlex.
- Meta-evolutionary exome analysis identifies novel type 2 diabetes mellitus genes in the UK Biobank and all of us.PLoS genetics · 2025Pooled it
- Unraveling Atherosclerosis through Multi-omics: Systematic Insights into the Unique Applications and Clinical Perspectives.Current atherosclerosis reports · 2026Review
- Ultra-rare functional variants reveal early-onset breast cancer risk genes and pathways in the UK Biobank and All of Us Research Program.American journal of human genetics · 2026Article
- Integrative exome sequencing and machine learning identify MICB and interferon pathway genes as contributors to SSc risk.Annals of the rheumatic diseases · 2025Article
- Machine learning and multi-omics integration: advancing cardiovascular translational research and clinical practice.Journal of translational medicine · 2025Review
- Article
- Construction of machine learning diagnostic models for cardiovascular pan-disease based on blood routine and biochemical detection data.Cardiovascular diabetology · 2024Article
- The relationship between early-onset coronary artery disease and familial hypercholesterolemia: a cohort study based on the Hakka population in Meizhou, China.American journal of translational research · 2024Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
Background Coronary artery disease is a primary cause of death around the world, with both genetic and environmental risk factors. Although genome-wide association studies have linked >100 unique loci to its genetic basis, these only explain a fraction of disease heritability. Methods and Results To find additional gene drivers of coronary artery disease, we applied machine learning to quantitative evolutionary information on the impact of coding variants in whole exomes from the Myocardial Infarction Genetics Consortium. Using ensemble-based supervised learning, the Evolutionary Action-Machine Learning framework ranked each gene's ability to classify case and control samples and identified 79 significant associations. These were connected to known risk loci; enriched in cardiovascular processes like lipid metabolism, blood clotting, and inflammation; and enriched for cardiovascular phenotypes in knockout mouse models. Among them,
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Registered trials
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