A k-mer based metaheuristic approach for detecting COVID-19 variants

Abstract

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) belongs to coronaviridae family and a change in the genetic sequence of SARS-CoV-2 is named as a mutation that causes to variants of SARS-CoV-2. In this paper, we propose a novel and efficient method to predict SARS-CoV-2 variants of concern from whole human genome sequences. In this method, we describe 16 dinucleotide and 64 trinucleotide features to differentiate SARS-CoV-2 variants of concern. The efficacy of the proposed features is proved by using four classifiers, k-nearest neighbor, support vector machines, multilayer perceptron, and random forest. The proposed method is evaluated on the dataset including 223,326 complete human genome sequences including recently designated variants of concern, Alpha, Beta, Gamma, Delta, and Omicron variants. Experimental results present that overall accuracy for detecting SARS-CoV-2 variants of concern remarkably increases when trinucleotide features rather than dinucleotide features are used. Furthermore, we use the whale optimization algorithm, which is a state-of-the-art method for reducing the number of features and choosing the most relevant features. We select 44 trinucleotide features out of 64 to differentiate SARS-CoV-2 variants with acceptable accuracy as a result of the whale optimization method. Experimental results indicate that the SVM classifier with selected features achieves about 99% accuracy, sensitivity, specificity, precision on average. The proposed method presents an admirable performance for detecting SARS-CoV-2 variants.

Keywords

• COVID-19
• SARS-CoV-2
• Whale Optimization Algorithm
• Classifiers
• Feature Selection
• Machine Learning

COVID-19 varyantlarını tespit etmek için k-mer tabanlı bir metasezgisel yaklaşım

Abstract

Emergence of SARS-CoV-2 variants threatens the public health and remarkably prolong the COVID-19 pandemic. Rapid and accurate detection of SARS-CoV-2 variants is crucial to track mutations, monitor the changes, measure the efficiency of the current vaccines, assess the evolution of SARS-CoV-2 as well as prevent its spread. In this paper, we propose a novel and efficient method to predict SARS-CoV-2 variants of concern from whole human genome sequences. In this method, we describe 16 dinucleotide and 64 trinucleotide features to differentiate SARS-CoV-2 variants of concern. The efficacy of the proposed features is proved by using four classifiers, k-nearest neighbor, support vector machines, multilayer perceptron, and random forest. The proposed method is evaluated on the dataset including 223,326 complete human genome sequences including recently designated variants of concern, Alpha, Beta, Gamma, Delta, and Omicron variants. Experimental results present that overall accuracy for detecting SARS-CoV-2 variants of concern remarkably increases when trinucleotide features rather than dinucleotide features are used. Furthermore, we use the whale optimization algorithm, which is the state-of-the-art method for reducing the number of features and choosing the most relevant features. We select 44 trinucleotide features out of 64 to differentiate SARS-CoV-2 variants with acceptable accuracy as a result of the whale optimization method. Experimental results indicate that the SVM classifier with selected features achieves about 99% accuracy, sensitivity, specificity, precision on average. The proposed method presents an admirable performance for detecting SARS-CoV-2 variants.

Keywords

• COVID-19
• SARS-CoV-2
• Classifiers
• Feature Selection
• Machine Learning

References

1. [1] Volz, E., Mishra, S., Chand, M., Barrett, J. C., & al., R. J. et. (2021). Assessing transmissibility of SARS-CoV-2 lineage B.1.1.7 in England. Nature, 593(7858), 266–269. doi:10.1038/s41586-021-03470-x
2. [2] Lauring, A. S., & Malani, P. N. (09 2021). Variants of SARS-CoV-2. JAMA, 326(9), 880–880. doi:10.1001/jama.2021.14181
3. [3] Tegally, H., Wilkinson, E., Giovanetti, M., & al., A. I. et. (2021). Detection of a SARS-CoV-2 variant of concern in South Africa. Nature, 592(7854), 438–443. doi:10.1038/s41586-021-03402-9
4. [4] Sabino, E. C., Buss, L. F., Carvalho, M. P. S., & al., E. (2021). Resurgence of COVID-19 in Manaus, Brazil, despite high seroprevalence. The Lancet, 397(10273), 452–455. doi:10.1016/s0140-6736(21)00183-5
5. [5] Mlcochova, P., Kemp, S. A., Dhar, M. S., & al., G. P. et. (2021). SARS-CoV-2 B.1.617.2 Delta variant replication and immune evasion. Nature, 599(7883), 114–119. doi:10.1038/s41586-021-03944-y
6. [6] Sahoo, J. P., & Samal, K. C. (2021). World on alert: WHO designated south African new COVID strain (Omicron/B.1.1.529) as a variant of concern. Biotica Research Today, 3(11), 1086–1088.
7. [7] Jiang, X., Coffee, M., Bari, A., Wang, J., Jiang, X., Huang, J., … Huang, Y. (2020). Towards an Artificial Intelligence Framework for Data-Driven Prediction of Coronavirus Clinical Severity. Computers, Materials $\&$ Continua, 62(3), 537–551. doi:10.32604/cmc.2020.010691
8. [8] Zoabi, Y., Deri-Rozov, S., & Shomron, N. (2021). Machine learning-based prediction of COVID-19 diagnosis based on symptoms. Npj Digital Medicine, 4(1), 3. doi:10.1038/s41746-020-00372-6

9. [9] Muhammad, L. J., Algehyne, E. A., Usman, S. S., Ahmad, A., Chakraborty, C., & Mohammed, I. A. (2021). Supervised Machine Learning Models for Prediction of COVID-19 Infection using Epidemiology Dataset. SN Computer Science, 2(1), 11. doi:10.1007/s42979-020-00394-7
10. [10] Shi, F., Wang, J., Shi, J., Wu, Z., Wang, Q., Tang, Z., … Shen, D. (2021). Review of Artificial Intelligence Techniques in Imaging Data Acquisition, Segmentation, and Diagnosis for COVID-19. IEEE Reviews in Biomedical Engineering, 14, 4–15. doi:10.1109/RBME.2020.2987975
11. [11] Mohamadou, Y., Halidou, A., & Kapen, P. T. (2020). A review of mathematical modeling, artificial intelligence and datasets used in the study, prediction and management of COVID-19. Applied Intelligence, 50(11), 3913–3925. doi:10.1007/s10489-020-01770-9
12. [12] Arslan, H., & Arslan, H. (2021). A new COVID-19 detection method from human genome sequences using CpG island features and KNN classifier. Engineering Science and Technology, an International Journal. doi:10.1016/j.jestch.2020.12.026
13. [13] Arslan, H. (2021a). COVID-19 prediction based on genome similarity of human SARS-CoV-2 and bat SARS-CoV-like coronavirus. Computers $\&$ Industrial Engineering, 161, 107666. doi:10.1016/j.cie.2021.107666
14. [14] Arslan, H., & Aygün, B. (2021). Performance Analysis of Machine Learning Algorithms in Detection of COVID-19 from Common Symptoms. 2021 29th Signal Processing and Communications Applications Conference (SIU), 1–4. doi:10.1109/SIU53274.2021.9477809
15. [15] Arslan, H. (2021b). Machine Learning Methods for COVID-19 Prediction Using Human Genomic Data. Proceedings, 74(1). doi:10.3390/proceedings2021074020
16. [16] Ali, S., Tamkanat-E-Ali, Khan, M. A., Khan, I., & Patterson, M. (2021). Effective and scalable clustering of SARS-CoV-2 sequences. arXiv [q-bio.PE]. Ανακτήθηκε από http://arxiv.org/abs/2108.08143
17. [17] Jamil, S., & Rahman, M. (2021). A Dual-Stage Vocabulary of Features (VoF)-Based Technique for COVID-19 Variants’ Classification. Applied Sciences, 11(24). doi:10.3390/app112411902
18. [18] Ogiela, M. R., & Ogiela, U. (2021). Linguistic methods in healthcare application and COVID-19 variants classification. Neural Computing and Applications. doi:10.1007/s00521-021-06286-y
19. [19] Mann, C., Griffin, J. H., & Downard, K. M. (2021). Detection and evolution of SARS-CoV-2 coronavirus variants of concern with mass spectrometry. Analytical and Bioanalytical Chemistry, 413(29), 7241–7249. doi:10.1007/s00216-021-03649-1
20. [20] Mafarja, M., & Mirjalili, S. (2018). Whale optimization approaches for wrapper feature selection. Applied Soft Computing, 62, 441–453. doi:10.1016/j.asoc.2017.11.006
21. [21] Brown, I., & Mues, C. (2012). An experimental comparison of classification algorithms for imbalanced credit scoring data sets. Expert Systems with Applications, 39(3), 3446–3453.
22. [22] Deng, Z., Zhu, X., Cheng, D., Zong, M., & Zhang, S. (2016). Efficient KNN Classification Algorithm for Big Data. Neurocomput., 195(C), 143–148. doi:10.1016/j.neucom.2015.08.112
23. [23] Abu Alfeilat, H., Hassanat, A., Lasassmeh, O., Tarawneh, A., Alhasanat, M., Eyal-Salman, H., & Prasath, S. (08 2019). Effects of Distance Measure Choice on K-Nearest Neighbor Classifier Performance: A Review. Big Data, 7. doi:10.1089/big.2018.0175
24. [24] Bishop, C. M. (2006). Pattern recognition and Machine Learning. Springer.
25. [25] Hornik, K., Stinchcombe, M., & White, H. (1989). Multilayer feedforward networks are universal approximators. Neural Networks, 2(5), 359–366. doi:10.1016/0893-6080(89)90020-8
26. [26] Burges, C. J. C. (1998). A Tutorial on Support Vector Machines for Pattern Recognition. Data Mining and Knowledge Discovery, 2(2), 121–167. doi:10.1023/A:1009715923555
27. [27] Vapnik, V. N. (1995). The Nature of Statistical Learning Theory. doi:10.1007/978-1-4757-2440-0
28. [28] Hsu, C.-W., & Lin, C.-J. (2002). A comparison of methods for multiclass support vector machines. IEEE Transactions on Neural Networks, 13(2), 415–425. doi:10.1109/72.991427
29. [29] Min, J. H., & Lee, Y.-C. (2005). Bankruptcy prediction using support vector machine with optimal choice of kernel function parameters. Expert Systems with Applications, 28(4), 603–614. doi:10.1016/j.eswa.2004.12.008
30. [30] Keerthi, S. S., & Lin, C.-J. (2003). Asymptotic Behaviors of Support Vector Machines with Gaussian Kernel. Neural Computation, 15(7), 1667–1689. doi:10.1162/089976603321891855
31. [31] Breiman, L. (2001a). Random Forests. Machine Learning, 45(1), 5–32. doi:10.1023/A:1010933404324
32. [32] Breiman, L. (2001b). Machine Learning, 45(1), 5–32. doi:10.1023/a:1010933404324
33. [33] Shu, Y., & McCauley, J. (2017). GISAID: Global initiative on sharing all influenza data - from vision to reality. Eurosurveillance, 22(13). doi:10.2807/1560-7917.ES.2017.22.13.30494
34. [34] Sokolova, M., & Lapalme, G. (2009). A systematic analysis of performance measures for classification tasks. Information Processing $\&$ Management, 45(4), 427–437. doi:10.1016/j.ipm.2009.03.002