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<lom:langstring xml:lang="x-none">10.3390/microorganisms12040798</lom:langstring>

  
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<lom:langstring xml:lang="en">Machine Learning to Identify Critical Biomarker Profiles in New SARS-CoV-2 Variants</lom:langstring>

  
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<lom:langstring xml:lang="en">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&#39;s translation machinery.</lom:langstring>

  
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<lom:langstring xml:lang="en">Alpha</lom:langstring>

  
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<lom:langstring xml:lang="en">Alpha + E484K</lom:langstring>

  
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<lom:langstring xml:lang="en">Restricted Boltzmann Machine Neural Network</lom:langstring>

  
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<lom:datetime>2025-08-22T09:25:00.484Z</lom:datetime>

  
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N:Lee;Hye Kyung;
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N:Posch;Wilfried;
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N:Haybaeck;Johannes;
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