Microorganisms (Apr 2024)

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

  • 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

DOI
https://doi.org/10.3390/microorganisms12040798
Journal volume & issue
Vol. 12, no. 4
p. 798

Abstract

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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.

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