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

ÇOLAK, Cemil; Akbulut, Sami; YAĞIN, Fatma Hilal

doi:10.14744/etd.2022.00868

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

AHMET SAMİ AKBULUT, (Cerrahi Bölümü, İnönü Üniversitesi Tıp Fakültesi, Malatya, Türkiye)

Fatma Hilal YAĞIN, (Biyoistatistik Bölümü ve Tıp Bilişimi, İnönü Üniversitesi Tıp Fakültesi, Malatya, Türkiye)

Cemil ÇOLAK (Biyoistatistik Bölümü ve Tıp Bilişimi, İnönü Üniversitesi Tıp Fakültesi, Malatya, Türkiye)

Erciyes Medical Journal

3 1

Yıl: 2022 Cilt: 44 Sayı: 6 Sayfa Aralığı: 544 - 548 Metin Dili: İngilizce DOI: 10.14744/etd.2022.00868 İndeks Tarihi: 20-05-2023

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

Öz:

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 air- way 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.

Anahtar Kelime:

Belge Türü: Makale Makale Türü: Araştırma Makalesi Erişim Türü: Erişime Açık

1. Sahin TT, Akbulut S, Yilmaz S. COVID-19 pandemic: Its impact on liver disease and liver transplantation. World J Gastroenterol 2020; 26(22): 2987–99.
2. Myoung J. Two years of COVID-19 pandemic: where are we now?. J Microbiol 2022; 60(3): 235–7.
3. Huang C, Wang Y, Li X, Ren L, Zhao J, Hu Y, et al. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. The lancet 2020; 395(10223): 497–506.
4. Yan L, Zhang HT, Goncalves J, Xiao Y, Wang M, Guo Y, et al. An in- terpretable mortality prediction model for COVID-19 patients. Nature machine intelligence 2020; 2(5): 283–8.
5. Yagin FH, Güldoğan E, Ucuzal H, Colak C. A computer-assisted di- agnosis tool for classifying COVID-19 based on Chest X-Ray images. Konuralp Med J 2021; 13(S1): 438–45.
6. Arentz M, Yim E, Klaff L, Lokhandwala S, Riedo FX, Chong M, et al. Characteristics and outcomes of 21 critically ill patients with COVID-19 in Washington State. JAMA 2020; 323(16): 1612–4.
7. Shi F, Wang J, Shi J, Wu Z, Wang Q, Tang Z, et al. Review of artificial intelligence techniques in imaging data acquisition, segmentation, and diagnosis for COVID-19. IEEE Rev Biomed Eng 2021; 14: 4–15.
8. Hoque MN, Rahman MS, Ahmed R, Hossain MS, Islam MS, Islam T, et al. Diversity and genomic determinants of the microbiomes associ- ated with COVID-19 and non-COVID respiratory diseases. Gene Rep 2021; 23: 101200.
9. Zhang Y, Pan Y, Zhao X, Shi W, Chen Z, Zhang S, et al. Genomic characterization of SARS-CoV-2 identified in a reemerging COVID-19 outbreak in Beijing’s Xinfadi market in 2020. Biosaf Health 2020; 2(4): 202–5.
10. Ding W, Nayak J, Swapnarekha H, Abraham A, Naik B, Pelusi D. Fusion of intelligent learning for COVID-19: A state-of-the-art re- view and analysis on real medical data. Neurocomputing 2021; 457: 40–66.
11. Mostafa HH, Fissel JA, Fanelli B, Bergman Y, Gniazdowski V, Dadlani M, et al. metagenomic next-generation sequencing of nasopharyngeal specimens collected from confirmed and suspect COVID-19 patients. mBio 2020; 11(6): e01969–20.
12. Perçin İ, Yağin FH, Güldoğan E, Yoloğlu S. ARM: An interactive web software for association rules mining and an application in medicine. 2019 International Artificial Intelligence and Data Processing Sympo- sium (IDAP). IEEE; 2019. pp.1–5.
13. Bellini V, Valente M, Bertorelli G, Pifferi B, Craca M, Mordonini M, et al. Machine learning in perioperative medicine: a systematic review. J Anesthesia, Analgesia and Critical Care 2022; 2(1): 1–13.
14. Yagin FH, Yagin B, Arslan AK, Colak C. Comparison of performances of associative classification methods for cervical cancer prediction: Ob- servational study. Turkiye Klinikleri J Biostatistics 2021; 13(3): 266– 72.
15. Shiri I, Salimi Y, Pakbin M, Hajianfar G, Avval AH, Sanaat A, et al. COVID-19 prognostic modeling using CT radiomic features and ma- chine learning algorithms: Analysis of a multi-institutional dataset of 14,339 patients. Comput Biol Med 2022; 145: 105467.
16. Abubaker Bagabir S, Ibrahim NK, Abubaker Bagabir H, Hashem Ateeq R. Covid-19 and Artificial Intelligence: Genome sequencing, drug de- velopment and vaccine discovery. J Infect Public Health 2022; 15(2): 289–96.
17. Suvarna K, Biswas D, Pai MGJ, Acharjee A, Bankar R, Palanivel V, et al. Proteomics and machine learning approaches reveal a set of prog- nostic markers for COVID-19 severity with drug repurposing potential. Front Physiol 2021; 12: 652799.
18. Mick E, Kamm J, Pisco AO, Ratnasiri K, Babik JM, Castañeda Get al. Upper airway gene expression reveals suppressed immune responses to SARS-CoV-2 compared with other respiratory viruses. Nat Com- mun 2020; 11(1): 5854.
19. Li Z, Guo W, Ding S, Feng K, Lu L, Huang T, et al. Detecting blood methylation signatures in response to childhood cancer radiotherapy via machine learning methods. Biology (Basel) 2022; 11(4): 607.
20. He S, Wu J, Wang D, He X. Predictive modeling of groundwater ni- trate pollution and evaluating its main impact factors using random forest. Chemosphere 2022; 290: 133388.
21. Palimkar P, Shaw RN, Ghosh A. Machine learning technique to prog- nosis diabetes disease: random forest classifier approach. Advanced Computing and Intelligent Technologies. Springer; 2022.p.219–44.
22. Larestani A, Mousavi SP, Hadavimoghaddam F, Hemmati-Sarapardeh A. Predicting formation damage of oil fields due to mineral scaling during water-flooding operations: Gradient boosting decision tree and cascade-forward back-propagation network. J Petroleum Scien Engi- neer 2022; 208(A): 109315.
23. Kruse R, Mostaghim S, Borgelt C, Braune C, Steinbrecher M. Multi- layer perceptrons. Computational intelligence. Springer; 2022.p.53– 124.
24. Xu Y, Li F, Asgari A. Prediction and optimization of heating and cool- ing loads in a residential building based on multi-layer perceptron neu- ral network and different optimization algorithms. Energy 2022; 240: 122692.
25. Cohen J. The world is round (p<. 05). American Psychologist 1994; 49(12): 997–1003.
26. Shojaei M, Shamshirian A, Monkman J, Grice L, Tran M, Tan CW, et al. IFI27 transcription is an early predictor for COVID-19 out- comes; a multi-cohort observational study. medRxiv 2021. Avail- able from: URL: https://www.medrxiv.org/content/10.1101/ 2021.10.29.21265555v1.
27. Hasankhani A, Bahrami A, Sheybani N, Aria B, Hemati B, Fatehi F, et al. Differential co-expression network analysis reveals key hub-high traffic genes as potential therapeutic targets for COVID-19 pandemic. Front Immunol 2021; 12: 789317.
28. Zhang YH, Li H, Zeng T, Chen L, Li Z, Huang T, et al. Identifying transcriptomic signatures and rules for SARS-CoV-2 infection. Front Cell Dev Biol 2021; 8: 627302.

APA	Akbulut S, YAĞIN F, ÇOLAK C (2022). Prediction of COVID-19 Based on Genomic Biomarkers of Metagenomic Next-Generation Sequencing Data Using Artificial Intelligence Technology. , 544 - 548. 10.14744/etd.2022.00868
Chicago	Akbulut Sami,YAĞIN Fatma Hilal,ÇOLAK Cemil Prediction of COVID-19 Based on Genomic Biomarkers of Metagenomic Next-Generation Sequencing Data Using Artificial Intelligence Technology. (2022): 544 - 548. 10.14744/etd.2022.00868
MLA	Akbulut Sami,YAĞIN Fatma Hilal,ÇOLAK Cemil Prediction of COVID-19 Based on Genomic Biomarkers of Metagenomic Next-Generation Sequencing Data Using Artificial Intelligence Technology. , 2022, ss.544 - 548. 10.14744/etd.2022.00868
AMA	Akbulut S,YAĞIN F,ÇOLAK C Prediction of COVID-19 Based on Genomic Biomarkers of Metagenomic Next-Generation Sequencing Data Using Artificial Intelligence Technology. . 2022; 544 - 548. 10.14744/etd.2022.00868
Vancouver	Akbulut S,YAĞIN F,ÇOLAK C Prediction of COVID-19 Based on Genomic Biomarkers of Metagenomic Next-Generation Sequencing Data Using Artificial Intelligence Technology. . 2022; 544 - 548. 10.14744/etd.2022.00868
IEEE	Akbulut S,YAĞIN F,ÇOLAK C "Prediction of COVID-19 Based on Genomic Biomarkers of Metagenomic Next-Generation Sequencing Data Using Artificial Intelligence Technology." , ss.544 - 548, 2022. 10.14744/etd.2022.00868
ISNAD	Akbulut, Sami vd. "Prediction of COVID-19 Based on Genomic Biomarkers of Metagenomic Next-Generation Sequencing Data Using Artificial Intelligence Technology". (2022), 544-548. https://doi.org/10.14744/etd.2022.00868

APA	Akbulut S, YAĞIN F, ÇOLAK C (2022). Prediction of COVID-19 Based on Genomic Biomarkers of Metagenomic Next-Generation Sequencing Data Using Artificial Intelligence Technology. Erciyes Medical Journal, 44(6), 544 - 548. 10.14744/etd.2022.00868
Chicago	Akbulut Sami,YAĞIN Fatma Hilal,ÇOLAK Cemil Prediction of COVID-19 Based on Genomic Biomarkers of Metagenomic Next-Generation Sequencing Data Using Artificial Intelligence Technology. Erciyes Medical Journal 44, no.6 (2022): 544 - 548. 10.14744/etd.2022.00868
MLA	Akbulut Sami,YAĞIN Fatma Hilal,ÇOLAK Cemil Prediction of COVID-19 Based on Genomic Biomarkers of Metagenomic Next-Generation Sequencing Data Using Artificial Intelligence Technology. Erciyes Medical Journal, vol.44, no.6, 2022, ss.544 - 548. 10.14744/etd.2022.00868
AMA	Akbulut S,YAĞIN F,ÇOLAK C Prediction of COVID-19 Based on Genomic Biomarkers of Metagenomic Next-Generation Sequencing Data Using Artificial Intelligence Technology. Erciyes Medical Journal. 2022; 44(6): 544 - 548. 10.14744/etd.2022.00868
Vancouver	Akbulut S,YAĞIN F,ÇOLAK C Prediction of COVID-19 Based on Genomic Biomarkers of Metagenomic Next-Generation Sequencing Data Using Artificial Intelligence Technology. Erciyes Medical Journal. 2022; 44(6): 544 - 548. 10.14744/etd.2022.00868
IEEE	Akbulut S,YAĞIN F,ÇOLAK C "Prediction of COVID-19 Based on Genomic Biomarkers of Metagenomic Next-Generation Sequencing Data Using Artificial Intelligence Technology." Erciyes Medical Journal, 44, ss.544 - 548, 2022. 10.14744/etd.2022.00868
ISNAD	Akbulut, Sami vd. "Prediction of COVID-19 Based on Genomic Biomarkers of Metagenomic Next-Generation Sequencing Data Using Artificial Intelligence Technology". Erciyes Medical Journal 44/6 (2022), 544-548. https://doi.org/10.14744/etd.2022.00868