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Martina Brueckner

Yale University

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Scholarly Papers (1)

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Using Machine Learning to Predict Noncoding Variant Associations with Sulcal Patterns in Congenital Heart Disease

Number of pages: 35 Posted: 30 May 2024
Harvard University - Boston Children’s Hospital, Harvard University - Harvard Medical School, Harvard University - Harvard Medical School, Yale University, University of Southern California, Harvard University - Harvard Medical School, Icahn School of Medicine at Mount Sinai - Department of Genetics and Genomic Sciences, Mindich Child Health and Development Institute, and Department of Pediatrics, University of Pennsylvania - Children's Hospital of Philadelphia, University of California, San Diego (UCSD), University of Pennsylvania - Children's Hospital of Philadelphia, University of California, San Francisco (UCSF), University of Utah, University of Pennsylvania - Children's Hospital of Philadelphia, University of Rochester Medical Center, Harvard University - Harvard Medical School, Harvard University - Harvard Medical School, University of Michigan at Ann Arbor, University of Utah - School of Medicine, Harvard University - Harvard Medical School, Harvard University - Harvard Medical School and Harvard University - Division of Newborn Medicine
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Abstract:

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brain magnetic resonance imaging, congenital heart disease, deep learning, executive function, noncoding variant, sulcal pattern