Rapidly identifying new coronavirus mutations of potential concern in the Omicron variant using an unsupervised learning strategy

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Extensive mutations in the Omicron spike protein appear to accelerate the transmission of SARS-CoV-2, and rapid infections increase the odds that additional mutants will emerge. To build an investigative framework, we have applied an unsupervised machine learning approach to 4296 Omicron viral genom...

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Detalles Bibliográficos
Autores principales: Zhao, Lue Ping, Lybrand, Terry P., Gilbert, Peter B., Payne, Thomas H., Pyo, Chul-Woo, Geraghty, Daniel E., Jerome, Keith R.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9645309/
https://www.ncbi.nlm.nih.gov/pubmed/36352021
http://dx.doi.org/10.1038/s41598-022-23342-2

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9645309/
https://www.ncbi.nlm.nih.gov/pubmed/36352021
http://dx.doi.org/10.1038/s41598-022-23342-2

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