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

Christoph A Schatz; Ludwig Knabl Sr; 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:10.3390/microorganisms12040798

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Title (eng)

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

Author

Christoph A Schatz

Ludwig Knabl Sr

Hye Kyung Lee

Rita Seeboeck

Dorothee von Laer

Eliott Lafon

Wegene Borena

Harald Mangge

Florian Prüller

Adelina Qerimi

Doris Wilflingseder

University of Veterinary Medicine Vienna

Wilfried Posch

Johannes Haybaeck

Abstract (eng)

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.

Keywords (eng)

SARS-CoV-2Vaccination StateVariantsAlphaAlpha + E484KBetaOmicronZ-ScoresPC AlgorithmPrecisionRecallF1 ScoreMachine LearningRestricted Boltzmann Machine Neural Network

Type (eng)

journal article

Language

[eng]

Persistent identifier

https://phaidra.vetmeduni.ac.at/o:4401

DOI

10.3390/microorganisms12040798

Is in series

Title (eng)

Microorganisms

Volume