Machine Learning to Identify Critical Biomarker Profiles in New SARS-CoV-2 Variants

Autor: Christoph Schatz, Ludwig Knabl, Hye Kyung Lee, Rita Seeboeck, Dorothee von Laer, Eliott Lafon, Wegene Borena, Harald Mangge, Florian Prüller, Adelina Qerimi, Doris Wilflingseder, Wilfried Posch, Johannes Haybaeck
Jazyk: angličtina
Rok vydání: 2024
Předmět:
Zdroj: Microorganisms, Vol 12, Iss 4, p 798 (2024)
Druh dokumentu: article
ISSN: 2076-2607
DOI: 10.3390/microorganisms12040798
Popis: The global dissemination of SARS-CoV-2 resulted in the emergence of several variants, including Alpha, Alpha + E484K, Beta, and Omicron. Our research integrated the study of eukaryotic translation factors and fundamental components in general protein synthesis with the analysis of SARS-CoV-2 variants and vaccination status. Utilizing statistical methods, we successfully differentiated between variants in infected individuals and, to a lesser extent, between vaccinated and non-vaccinated infected individuals, relying on the expression profiles of translation factors. Additionally, our investigation identified common causal relationships among the translation factors, shedding light on the interplay between SARS-CoV-2 variants and the host’s translation machinery.
Databáze: Directory of Open Access Journals