Large-scale machine-learning-based phenotyping significantly improves genomic discovery for optic nerve head morphology

Genome-wide association studies (GWASs) require accurate cohort phenotyping, but expert labeling can be costly, time intensive, and variable. Here, we develop a machine learning (ML) model to predict glaucomatous optic nerve head features from color fundus photographs. We used the model to predict v...

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Detalles Bibliográficos
Autores principales: Alipanahi, Babak, Hormozdiari, Farhad, Behsaz, Babak, Cosentino, Justin, McCaw, Zachary R., Schorsch, Emanuel, Sculley, D., Dorfman, Elizabeth H., Foster, Paul J., Peng, Lily H., Phene, Sonia, Hammel, Naama, Carroll, Andrew, Khawaja, Anthony P., McLean, Cory Y.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8322934/
https://www.ncbi.nlm.nih.gov/pubmed/34077760
http://dx.doi.org/10.1016/j.ajhg.2021.05.004

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8322934/
https://www.ncbi.nlm.nih.gov/pubmed/34077760
http://dx.doi.org/10.1016/j.ajhg.2021.05.004

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