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Epigenetic patient stratification via contrastive machine learning refines hallmark biomarkers in minoritized children with asthma

Year: 2024
Type: preprint
Study design: Observational Study
DOI: 10.21203/rs.3.rs-5066762/v1
Authors: Elior Rahmani, Aditya Gorla, Esteban Burchard, Jonathan Witonsky, Noah Zaitlen, Jennifer Elhawary, Zeyuan Chen, Joel Mefford, Javier Perez-Garcia, Scott Huntsman, Donglei Hu, Celeste Eng, Prescott Woodruff, Sriram Sankararaman, Elad Ziv, Jonathan Flint
Language: English
Subfield: Physiology
Field: Medicine
Open Access status: green
Funders: Robert Wood Johnson Foundation, Tobacco-Related Disease Research Program, American Asthma Foundation, National Institutes of Health, National Heart, Lung, and Blood Institute, National Institute of Mental Health, National Institute on Minority Health and Health Disparities
Awards: G1069245730, G1403217634, G2189466114, G3084499258, Epigenomics of asthma risk factors and clinical subtypes in minority children, G4183848059, Subtyping complex phenotypes via constrastive learning by leveraging electronic health records, G5272449015, Gene-Environment Analyses of Early Life Exposures and Asthma in Ethnically Diverse Children, Genetic Control of Airway Epithelium Gene Expression in Childhood Asthmatics, G6460128660, G7307112236, Genes, air pollution, and asthma severity in minority children, G9000025080, G9000032667, G9000115609, G9000143814, G9000227757, NRSA Training Core, Improving Prediction of Asthma-related Outcomes with Genetic Ancestry-informed Lung Function Equations
OpenAlex ID: W4402505838
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Cited by: 1
Related to: 10