https://phaidra.vetmeduni.ac.at/o:4401 Machine Learning to Identify Critical Biomarker Profiles in New SARS-CoV-2 Variants Schatz, Christoph A Christoph A Schatz Knabl Sr, Ludwig Ludwig Knabl Sr Lee, Hye Kyung Hye Kyung Lee Seeboeck, Rita Rita Seeboeck von Laer, Dorothee Dorothee von Laer Lafon, Eliott Eliott Lafon Borena, Wegene Wegene Borena Mangge, Harald Harald Mangge Prüller, Florian Florian Prüller Qerimi, Adelina Adelina Qerimi Wilflingseder, Doris Doris Wilflingseder 0000-0002-5888-5118 University of Veterinary Medicine Vienna Posch, Wilfried Wilfried Posch Haybaeck, Johannes Johannes Haybaeck MDPI journal article open access 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. 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 http://creativecommons.org/licenses/by/4.0/ https://phaidra.vetmeduni.ac.at/api/object/o:4401/download 10.3390/microorganisms12040798 https://phaidra.vetmeduni.ac.at/o:605 application/pdf Microorganisms issn:2076-2607 VoR Microorganisms 12 4 3.06 MB 2024