Genome-wide prediction of disease variant effects with a deep protein language model

Predicting the effects of coding variants is a major challenge. While recent deep-learning models have improved variant effect prediction accuracy, they cannot analyze all coding variants due to dependency on close homologs or software limitations. Here we developed a workflow using ESM1b, a 650-mil...

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Detalles Bibliográficos
Autores principales: Brandes, Nadav, Goldman, Grant, Wang, Charlotte H., Ye, Chun Jimmie, Ntranos, Vasilis
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group US 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10484790/
https://www.ncbi.nlm.nih.gov/pubmed/37563329
http://dx.doi.org/10.1038/s41588-023-01465-0

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10484790/
https://www.ncbi.nlm.nih.gov/pubmed/37563329
http://dx.doi.org/10.1038/s41588-023-01465-0

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