Unsupervised representation learning improves genomic discovery and risk prediction for respiratory and circulatory functions and diseases

High-dimensional clinical data are becoming more accessible in biobank-scale datasets. However, effectively utilizing high-dimensional clinical data for genetic discovery remains challenging. Here we introduce a general deep learning-based framework, REpresentation learning for Genetic discovery on...

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Detalles Bibliográficos
Autores principales: Yun, Taedong, Cosentino, Justin, Behsaz, Babak, McCaw, Zachary R., Hill, Davin, Luben, Robert, Lai, Dongbing, Bates, John, Yang, Howard, Schwantes-An, Tae-Hwi, Zhou, Yuchen, Khawaja, Anthony P., Carroll, Andrew, Hobbs, Brian D., Cho, Michael H., McLean, Cory Y., Hormozdiari, Farhad
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Cold Spring Harbor Laboratory 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10168505/
https://www.ncbi.nlm.nih.gov/pubmed/37163049
http://dx.doi.org/10.1101/2023.04.28.23289285

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10168505/
https://www.ncbi.nlm.nih.gov/pubmed/37163049
http://dx.doi.org/10.1101/2023.04.28.23289285

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