Sequencing meets machine learning to fight emerging pathogens: A preview

In searching for SARS-CoV variants-of-concern, pathogen sequencing is generating an impressive amount of data. However, beyond epidemiological use, these data contain cues fundamental to our understanding of pathogen evolution in the human population. Yet, to harness them, further development of com...

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Veröffentlicht in:Patterns (New York, N.Y.) N.Y.), 2022-02, Vol.3 (2), p.100448-100448, Article 100448
1. Verfasser: Yakimovich, Artur
Format: Artikel
Sprache:eng
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Zusammenfassung:In searching for SARS-CoV variants-of-concern, pathogen sequencing is generating an impressive amount of data. However, beyond epidemiological use, these data contain cues fundamental to our understanding of pathogen evolution in the human population. Yet, to harness them, further development of computational methodology, such as machine learning, may be required. This preview discusses updates in machine learning to understand emerging pathogens. In searching for SARS-CoV variants-of-concern, pathogen sequencing is generating an impressive amount of data. However, beyond epidemiological use, these data contain cues fundamental to our understanding of pathogen evolution in the human population. Yet, to harness them, further development of computational methodology, such as machine learning, may be required. This preview discusses updates in machine learning to understand emerging pathogens.
ISSN:2666-3899
2666-3899
DOI:10.1016/j.patter.2022.100448