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Article

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

Scientific reports - 9 Nov 2022

Zhao Lue Ping, Lybrand Terry P, Gilbert Peter B, Payne Thomas H, Pyo Chul-Woo, Geraghty Daniel E, Jerome Keith R

Abstract excerpt

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 genomes collected and deposited to GISAID as of December 14, 2021, and have identified a core haplotype of 28 polymutants...

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Source

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Identifiers

PMID: 36352021
DOI: 10.1038/s41598-022-23342-2
PMCID: PMC9645309

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026. Manifest publication-graph-cohort-2026-09-08.1.

Source receipt: pubmed, retrieved 7 Sept 2026.

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