{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,3]],"date-time":"2026-03-03T15:54:25Z","timestamp":1772553265379,"version":"3.50.1"},"reference-count":102,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2026,3,3]],"date-time":"2026-03-03T00:00:00Z","timestamp":1772496000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100018818","name":"National Research, Development and Innovation Office","doi-asserted-by":"publisher","award":["2020-2.1.1-ED-2020-00009"],"award-info":[{"award-number":["2020-2.1.1-ED-2020-00009"]}],"id":[{"id":"10.13039\/501100018818","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJMS"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/ijms27052358","type":"journal-article","created":{"date-parts":[[2026,3,3]],"date-time":"2026-03-03T12:48:56Z","timestamp":1772542136000},"page":"2358","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["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"],"prefix":"10.3390","volume":"27","author":[{"given":"Alexandra","family":"Neller","sequence":"first","affiliation":[{"name":"Department of Medical Genetics, University of Szeged, 6720 Szeged, Hungary"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"M\u00e1ty\u00e1s","family":"Bukva","sequence":"additional","affiliation":[{"name":"Biological Research Centre Szeged, 6726 Szeged, Hungary"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3949-7005","authenticated-orcid":false,"given":"Bence","family":"G\u00e1lik","sequence":"additional","affiliation":[{"name":"Hungarian Centre for Genomics and Bioinformatics, Szent\u00e1gothai Research Centre, University of P\u00e9cs, 7624 P\u00e9cs, Hungary"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9607-305X","authenticated-orcid":false,"given":"J\u00f3zsef","family":"Kun","sequence":"additional","affiliation":[{"name":"Hungarian Centre for Genomics and Bioinformatics, Szent\u00e1gothai Research Centre, University of P\u00e9cs, 7624 P\u00e9cs, Hungary"},{"name":"Department of Pharmacology and Pharmacotherapy, Medical School, University of P\u00e9cs, 7624 P\u00e9cs, 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Hungary"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-7900-9355","authenticated-orcid":false,"given":"Bal\u00e1zs","family":"Bende","sequence":"additional","affiliation":[{"name":"Department of Dermatology and Allergology, University of Szeged, 6720 Szeged, Hungary"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4043-3428","authenticated-orcid":false,"given":"P\u00e9ter","family":"Urb\u00e1n","sequence":"additional","affiliation":[{"name":"Hungarian Centre for Genomics and Bioinformatics, Szent\u00e1gothai Research Centre, University of P\u00e9cs, 7624 P\u00e9cs, Hungary"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Szilvia","family":"Kov\u00e1csn\u00e9 Levang","sequence":"additional","affiliation":[{"name":"Clinical Centre, First Department of Internal Medicine, University of P\u00e9cs, 6724 P\u00e9cs, 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Hungary"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0388-0942","authenticated-orcid":false,"given":"Katalin","family":"Gombos","sequence":"additional","affiliation":[{"name":"Hungarian Centre for Genomics and Bioinformatics, Szent\u00e1gothai Research Centre, University of P\u00e9cs, 7624 P\u00e9cs, Hungary"},{"name":"Department of Laboratory Medicine, Medical School, University of P\u00e9cs, 7624 P\u00e9cs, Hungary"},{"name":"Hungarian National Laboratory on Reproduction, University of P\u00e9cs, 7624 P\u00e9cs, Hungary"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Attila","family":"Gyenesei","sequence":"additional","affiliation":[{"name":"Hungarian Centre for Genomics and Bioinformatics, Szent\u00e1gothai Research Centre, University of P\u00e9cs, 7624 P\u00e9cs, 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