Zeitschriftenaufsatz | 2024 Open Access

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

Autor:in

Schatz, Christoph A.; Knabl Sr, Ludwig; Lee, Hye Kyung; Seeboeck, Rita; von Laer, Dorothee; Lafon, Eliott; Borena, Wegene; Mangge, Harald; Prüller, Florian; Qerimi, Adelina; Wilflingseder, Doris; Posch, Wilfried; Haybaeck, Johannes

Publikationen als Autor:in / Herausgeber:in der Vetmeduni

Wilflingseder, Doris

Journal

Microorganisms

Abstrakt

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.

Schlagwörter

SARS-CoV-2; vaccination state; variants; Alpha; Alpha+E484K; Beta; Omicron; z-scores; PC algorithm; precision; recall; F1 score; machine learning; Restricted Boltzmann Machine neural network

Dokumententyp

Originalarbeit

CC Lizenz

CCBY

Open Access Type

Gold

DOI

10.3390/microorganisms12040798