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

Christoph Schatz; Ludwig Knabl; 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

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

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

Institute of Clinical Pathology and Molecular Pathology of the Lower Austria Central Region (University Hospital St. Pölten)

Research output: Journal article (peer-reviewed) › Journal article

1 Citation (Scopus)

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.

Original language	English
Article number	798
Journal	Microorganisms
Volume	12
Issue number	4
DOIs	https://doi.org/10.3390/microorganisms12040798
Publication status	Published - 15 Apr 2024

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This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

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10.3390/microorganisms12040798

Cite this

@article{b3e867cc98634fb59f03e71b4ef3fae2,

title = "Machine Learning to Identify Critical Biomarker Profiles in New SARS-CoV-2 Variants",

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.",

author = "Christoph Schatz and Ludwig Knabl and Lee, \{Hye Kyung\} and Rita Seeboeck and \{von Laer\}, Dorothee and Eliott Lafon and Wegene Borena and Harald Mangge and Florian Pr{\"u}ller and Adelina Qerimi and Doris Wilflingseder and Wilfried Posch and Johannes Haybaeck",

note = "Publisher Copyright: {\textcopyright} 2024 by the authors.",

year = "2024",

month = apr,

day = "15",

doi = "10.3390/microorganisms12040798",

language = "English",

volume = "12",

journal = "Microorganisms",

issn = "2076-2607",

publisher = "Multidisciplinary Digital Publishing Institute (MDPI)",

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T1 - Machine Learning to Identify Critical Biomarker Profiles in New SARS-CoV-2 Variants

AU - Schatz, Christoph

AU - Knabl, Ludwig

AU - Lee, Hye Kyung

AU - Seeboeck, Rita

AU - von Laer, Dorothee

AU - Lafon, Eliott

AU - Borena, Wegene

AU - Mangge, Harald

AU - Prüller, Florian

AU - Qerimi, Adelina

AU - Wilflingseder, Doris

AU - Posch, Wilfried

AU - Haybaeck, Johannes

PY - 2024/4/15

Y1 - 2024/4/15

N2 - 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.

AB - 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.

UR - https://www.scopus.com/pages/publications/85191727349

U2 - 10.3390/microorganisms12040798

DO - 10.3390/microorganisms12040798

M3 - Journal article

C2 - 38674742

SN - 2076-2607

VL - 12

JO - Microorganisms

JF - Microorganisms

IS - 4

M1 - 798

ER -

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

Abstract

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Epigenetische Regulation der Expression von Eukaryotischen Translations Initiations Faktoren (eIFs) im Brustkrebs. Eine Pilotstudie

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

Abstract

UN SDGs

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Projects

Epigenetische Regulation der Expression von Eukaryotischen Translations Initiations Faktoren (eIFs) im Brustkrebs. Eine Pilotstudie

Cite this