Medizinische Universität Graz Austria/Österreich - Forschungsportal - Medical University of Graz
Gewählte Publikation:
SHR Neuro Krebs Kardio Lipid Stoffw Microb
Schatz, C; Knabl, L; Lee, HK; Seeboeck, R; von, Laer, D; Lafon, E; Borena, W; Mangge, H; Prüller, F; Qerimi, A; Wilflingseder, D; Posch, W; Haybaeck, J.
Machine Learning to Identify Critical Biomarker Profiles in New SARS-CoV-2 Variants.
Microorganisms. 2024; 12(4):
Doi: 10.3390/microorganisms12040798
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- Führende Autor*innen der Med Uni Graz
- Haybäck Johannes
- Co-Autor*innen der Med Uni Graz
- Mangge Harald
- Prüller Florian
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- Abstract:
- 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.
- Find related publications in this database (Keywords)
- SARS-CoV-2
- vaccination state
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- Alpha
- Alpha+E484K
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- Omicron
- z-scores
- PC algorithm
- precision
- recall
- F1 score
- machine learning
- Restricted Boltzmann Machine neural network