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

Research output: Journal article (peer-reviewed)Journal article


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.

Original languageEnglish
Article number798
Issue number4
Publication statusPublished - 15 Apr 2024


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