Genomic Surveillance of COVID-19 Variants With Language Models and Machine Learning.
SARS-CoV-2
genomic surveillance
natural language preprocessing
supervised predictions
unsupervised modeling
Journal
Frontiers in genetics
ISSN: 1664-8021
Titre abrégé: Front Genet
Pays: Switzerland
ID NLM: 101560621
Informations de publication
Date de publication:
2022
2022
Historique:
received:
19
01
2022
accepted:
14
03
2022
entrez:
25
4
2022
pubmed:
26
4
2022
medline:
26
4
2022
Statut:
epublish
Résumé
The global efforts to control COVID-19 are threatened by the rapid emergence of novel SARS-CoV-2 variants that may display undesirable characteristics such as immune escape, increased transmissibility or pathogenicity. Early prediction for emergence of new strains with these features is critical for pandemic preparedness. We present
Identifiants
pubmed: 35464852
doi: 10.3389/fgene.2022.858252
pii: 858252
pmc: PMC9024110
doi:
Types de publication
Journal Article
Langues
eng
Pagination
858252Informations de copyright
Copyright © 2022 Nagpal, Pal, Ashima, Tyagi, Tripathi, Nagori, Ahmad, Mishra, Malhotra, Kutum and Sethi.
Déclaration de conflit d'intérêts
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
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