Prediction of COVID-19 Based on Genomic Biomarkers of Metagenomic
Objective: 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. Materia...
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ftinonuuniv:oai:abakus.inonu.edu.tr:11616/86040 2023-05-15T18:11:20+02:00 Prediction of COVID-19 Based on Genomic Biomarkers of Metagenomic Next-Generation Sequencing Data Using Artificial Intelligence Technology Akbulut, S Yagin, FH Colak, C 2023-01-02T08:08:47Z http://hdl.handle.net/11616/86040 unknown http://hdl.handle.net/11616/86040 ERCIYES MEDICAL JOURNAL 2023 ftinonuuniv 2023-01-05T18:01:45Z Objective: 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. C1 [Akbulut, Sami] Inonu Univ, Fac Med, Dept Surg, Malatya, Turkey. [Akbulut, Sami] Inonu Univ, Fac Med, Dept Publ Hlth, Malatya, Turkey. [Akbulut, Sami; Yagin, Fatma Hilal; Colak, Cemil] Inonu Univ, Fac Med, Dept Biostat & Med Informat, Malatya, Turkey. C3 Inonu University; Inonu University; Inonu University Other/Unknown Material sami Unknown |
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Objective: 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. C1 [Akbulut, Sami] Inonu Univ, Fac Med, Dept Surg, Malatya, Turkey. [Akbulut, Sami] Inonu Univ, Fac Med, Dept Publ Hlth, Malatya, Turkey. [Akbulut, Sami; Yagin, Fatma Hilal; Colak, Cemil] Inonu Univ, Fac Med, Dept Biostat & Med Informat, Malatya, Turkey. C3 Inonu University; Inonu University; Inonu University |
author |
Akbulut, S Yagin, FH Colak, C |
spellingShingle |
Akbulut, S Yagin, FH Colak, C Prediction of COVID-19 Based on Genomic Biomarkers of Metagenomic |
author_facet |
Akbulut, S Yagin, FH Colak, C |
author_sort |
Akbulut, S |
title |
Prediction of COVID-19 Based on Genomic Biomarkers of Metagenomic |
title_short |
Prediction of COVID-19 Based on Genomic Biomarkers of Metagenomic |
title_full |
Prediction of COVID-19 Based on Genomic Biomarkers of Metagenomic |
title_fullStr |
Prediction of COVID-19 Based on Genomic Biomarkers of Metagenomic |
title_full_unstemmed |
Prediction of COVID-19 Based on Genomic Biomarkers of Metagenomic |
title_sort |
prediction of covid-19 based on genomic biomarkers of metagenomic |
publishDate |
2023 |
url |
http://hdl.handle.net/11616/86040 |
genre |
sami |
genre_facet |
sami |
op_source |
ERCIYES MEDICAL JOURNAL |
op_relation |
http://hdl.handle.net/11616/86040 |
_version_ |
1766184004430594048 |