Evidence map›Paper›PMID 41828576›Full record

ArticleInternational journal of molecular sciences2026

A Unique Patient Stratification Method Combined with a Machine Learning Approach Identifies Novel Genetic Susceptibility and Protective Factors for Severe COVID-19 in a Hungarian Population.

Alexandra Neller, Mátyás Bukva, Bence Gálik, József Kun, Nikoletta Nagy, Ferenc Somogyvári, Valéria Endrész, Margit Pál, Barbara Anna Bokor, Zsófia Blazovich and 9 more

Abstract read
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In one paragraph

Article in International journal of molecular sciences, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

19 authors.

Alexandra NellerDepartment of Medical Genetics, University of Szeged, 6720 Szeged, Hungary.
Mátyás BukvaBiological Research Centre Szeged, 6726 Szeged, Hungary.
Bence GálikHungarian Centre for Genomics and Bioinformatics, Szentágothai Research Centre, University of Pécs, 7624 Pécs, Hungary.ORCID 0000-0002-3949-7005
József KunHungarian Centre for Genomics and Bioinformatics, Szentágothai Research Centre, University of Pécs, 7624 Pécs, Hungary.ORCID 0000-0001-9607-305X
Nikoletta NagyDepartment of Medical Genetics, University of Szeged, 6720 Szeged, Hungary.ORCID 0000-0001-8576-7953
Ferenc SomogyváriDepartment of Medical Microbiology, University of Szeged, 6725 Szeged, Hungary.ORCID 0000-0001-8409-892X
Valéria EndrészDepartment of Medical Microbiology, University of Szeged, 6725 Szeged, Hungary.ORCID 0000-0002-9402-3857
Margit PálDepartment of Medical Genetics, University of Szeged, 6720 Szeged, Hungary.ORCID 0000-0003-3662-0837
Barbara Anna BokorDepartment of Medical Genetics, University of Szeged, 6720 Szeged, Hungary.
Zsófia BlazovichDepartment of Medical Genetics, University of Szeged, 6720 Szeged, Hungary.
Ádám VisnyovszkyDepartment of Dermatology and Allergology, University of Szeged, 6720 Szeged, Hungary.
Balázs BendeDepartment of Dermatology and Allergology, University of Szeged, 6720 Szeged, Hungary.ORCID 0009-0000-7900-9355
Péter UrbánHungarian Centre for Genomics and Bioinformatics, Szentágothai Research Centre, University of Pécs, 7624 Pécs, Hungary.ORCID 0000-0003-4043-3428
Szilvia Kovácsné LevangClinical Centre, First Department of Internal Medicine, University of Pécs, 6724 Pécs, Hungary.
Zoltán PéterfiClinical Centre, First Department of Internal Medicine, University of Pécs, 6724 Pécs, Hungary.ORCID 0000-0001-9658-153X
Gábor L KovácsHungarian Centre for Genomics and Bioinformatics, Szentágothai Research Centre, University of Pécs, 7624 Pécs, Hungary.
Katalin GombosHungarian Centre for Genomics and Bioinformatics, Szentágothai Research Centre, University of Pécs, 7624 Pécs, Hungary.ORCID 0000-0002-0388-0942
Attila GyeneseiHungarian Centre for Genomics and Bioinformatics, Szentágothai Research Centre, University of Pécs, 7624 Pécs, Hungary.
Márta SzéllDepartment of Medical Genetics, University of Szeged, 6720 Szeged, Hungary.

Funding

National Research, Development and Innovation Office 2020-2.1.1-ED-2020-00009
6 · The paper itself

Abstract

Intensive research has shown that severe COVID-19 outcomes are influenced by antiviral pathways and immune responses, both shaped by genetic predisposition. In this study, we aimed to identify genetic variants associated with disease severity in a cohort of Hungarian patients. We applied a novel stratification method based on age, disease severity, and clinical background to classify patients by susceptibility to severe COVID-19. Whole-exome sequencing (WES) was performed on 168 individuals, and gene mutation loads were assessed. Using a Random Forest machine learning approach, we identified variants of 877 genes that distinguished between severe and non-severe cases. We further categorized these genes as either susceptibility or protective factors. Gene-set enrichment analysis highlighted the most affected biological pathways. Our findings support the development of personalized diagnostic tools to assess the risk of severe COVID-19 and guide targeted treatment strategies. Our findings further extend the results of previous studies, providing novel insights into the genetic determinants of COVID-19 severity.

Indexed as

COVID-19Genetic Predisposition to DiseaseMachine LearningAdultAgedExome SequencingFemaleHumansHungaryMaleMiddle AgedMutationRandom ForestSARS-CoV-2Severity of Illness IndexCOVID-19genomicsimmunogeneticsmachine learning

Identifiers

PMID41828576
PMCPMC12986284

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.