Noncoding variants and sulcal patterns in congenital heart disease: Machine learning to predict functional impact
Mondragon-Estrada E, Newburger J, DePalma S, Brueckner M, Cleveland J, Chung W, Gelb B, Goldmuntz E, Hagler D, Huang H, McQuillen P, Miller T, Panigrahy A, Porter G, Roberts A, Rollins C, Russell M, Tristani-Firouzi M, Grant P, Im K, Morton S. Noncoding variants and sulcal patterns in congenital heart disease: Machine learning to predict functional impact. IScience 2025, 28: 111707. DOI: 10.1016/j.isci.2024.111707.Peer-Reviewed Original ResearchNoncoding variantsCongenital heart diseaseFunctions related to neuronal developmentGene regulatory signalsH3K9me2 modificationRegulatory signalsCongenital heart disease cohortsDevelopmental pathwaysNeuronal developmentFolding patternHeart diseaseFunctional impactGenetic factorsGenesVariantsBrain developmentPredictive impactSulcal patterns
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