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

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

Abstract 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 vi...

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Bibliographic Details
Main Authors: Lue Ping Zhao, Terry P. Lybrand, Peter B. Gilbert, Thomas H. Payne, Chul-Woo Pyo, Daniel E. Geraghty, Keith R. Jerome
Format: Article
Language:English
Published: Nature Portfolio 2022-11-01
Series:Scientific Reports
Online Access:https://doi.org/10.1038/s41598-022-23342-2

Internet

https://doi.org/10.1038/s41598-022-23342-2

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