Prediction of COVID-19 Based on Genomic Biomarkers of Metagenomic Next-Generation Sequencing Data Using Artificial Intelligence Technology

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dc.authoridAkbulut, Sami/0000-0002-6864-7711
dc.authoridÇOLAK, CEMİL/0000-0001-5406-098X
dc.authoridYagin, Fatma Hilal/0000-0002-9848-7958
dc.authorwosidAkbulut, Sami/L-9568-2014
dc.authorwosidÇOLAK, CEMİL/ABI-3261-2020
dc.authorwosidYagin, Fatma Hilal/ABI-8066-2020
dc.contributor.authorAkbulut, Sami
dc.contributor.authorYagin, Fatma Hilal
dc.contributor.authorColak, Cemil
dc.date.accessioned2024-08-04T20:11:41Z
dc.date.available2024-08-04T20:11:41Z
dc.date.issued2022
dc.departmentİnönü Üniversitesien_US
dc.description.abstractObjective: The primary aim of this study was to use metagenomic next-generation sequencing (mNGS) data to identify coronavirus 2019 (COVID-19)-related biomarker genes and to construct a machine learning model that could successfully differentiate patients with COVID-19 from healthy controls. Materials and Methods: The mNGS dataset used in the study demonstrated expression of 15,979 genes in the upper airway in 234 patients who were COVID-19 negative and COVID-19 positive. The Boruta method was used to select qualitative biomarker genes associated with COVID-19. Random forest (RF), gradient boosting tree (GBT), and multi-layer perceptron (MLP) models were used to predict COVID-19 based on the selected biomarker genes. Results: The MLP (0.936) model outperformed the GBT (0.851), and RF (0.809) models in predicting COVID-19. The three most important biomarker candidate genes associated with COVID-19 were IFI27, TPTI, and FAM83A. Conclusion: The proposed model (MLP) was able to predict COVID-19 successfully. The results showed that the generated model and selected biomarker candidate genes can be used as diagnostic models for clinical testing or potential therapeutic targets and vaccine design.en_US
dc.identifier.doi10.14744/etd.2022.00868
dc.identifier.issn2149-2247
dc.identifier.issn2149-2549
dc.identifier.trdizinid1173238en_US
dc.identifier.urihttps://doi.org/10.14744/etd.2022.00868
dc.identifier.urihttps://search.trdizin.gov.tr/yayin/detay/1173238
dc.identifier.urihttps://hdl.handle.net/11616/92917
dc.identifier.wosWOS:000821276800001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakTR-Dizinen_US
dc.language.isoenen_US
dc.publisherErciyes Univ Sch Medicineen_US
dc.relation.ispartofErciyes Medical Journalen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectArtificial intelligenceen_US
dc.subjectBorutaen_US
dc.subjectCOVID-19 pandemicen_US
dc.subjectfeature selectionen_US
dc.subjectmulti-layer perceptronen_US
dc.subjectSARS-CoV-2 virusen_US
dc.titlePrediction of COVID-19 Based on Genomic Biomarkers of Metagenomic Next-Generation Sequencing Data Using Artificial Intelligence Technologyen_US
dc.typeArticleen_US

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