Noncoding variants and sulcal patterns in congenital heart disease: Machine learning to predict functional impact

Noncoding variants and sulcal patterns in congenital heart disease: Machine learning to predict functional impact

Summary: Neurodevelopmental impairments associated with congenital heart disease (CHD) may arise from perturbations in brain developmental pathways, including the formation of sulcal patterns. While genetic factors contribute to sulcal features, the association of noncoding de novo variants (ncDNVs)...

Full description

Saved in:
Bibliographic Details
Main Authors: Enrique Mondragon-Estrada, Jane W. Newburger, Steven R. DePalma, Martina Brueckner, John Cleveland, Wendy K. Chung, Bruce D. Gelb, Elizabeth Goldmuntz, Donald J. Hagler, Jr., Hao Huang, Patrick McQuillen, Thomas A. Miller, Ashok Panigrahy, George A. Porter, Jr., Amy E. Roberts, Caitlin K. Rollins, Mark W. Russell, Martin Tristani-Firouzi, P. Ellen Grant, Kiho Im, Sarah U. Morton
Format: Article
Language:English
Published: Elsevier 2025-02-01
Series:iScience
Subjects:
Cardiovascular medicine
Machine learning
Online Access:http://www.sciencedirect.com/science/article/pii/S2589004224029341
Tags: Add Tag
No Tags, Be the first to tag this record!

Internet

http://www.sciencedirect.com/science/article/pii/S2589004224029341

Similar Items