Classification of SARS-CoV-2 and non-SARS-CoV-2 using machine learning algorithms. (September 2021)

• Record Type: Journal Article
• Authors: Singh, Om Prakash; Vallejo, Marta; El-Badawy, Ismail M.; Aysha, Ali; Madhanagopal, Jagannathan; Mohd Faudzi, Ahmad Athif
• Is Part Of: Computers in biology and medicine. Volume 136(2021)
• Publication Date: 2021-09
• Subjects: COVID-19 -- Signal processing -- Biomarker -- Machine learning; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2021.104650
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: Due to the continued evolution of the SARS-CoV-2 pandemic, researchers worldwide are working to mitigate, suppress its spread, and better understand it by deploying digital signal processing ( DSP ) and machine learning approaches. This study presents an alignment-free approach to classify the SARS-CoV-2 using complementary DNA, which is DNA synthesized from the single-stranded RNA virus. Herein, a total of 1582 samples, with different lengths of genome sequences from different regions, were collected from various data sources and divided into a SARS-CoV-2 and a non-SARS-CoV-2 group. We extracted eight biomarkers based on three-base periodicity, using DSP techniques, and ranked those based on a filter-based feature selection. The ranked biomarkers were fed into k-nearest neighbor, support vector machines, decision trees, and random forest classifiers for the classification of SARS-CoV-2 from other coronaviruses. The training dataset was used to test the performance of the classifiers based on accuracy and F-measure via 10-fold cross-validation. Kappa-scores were estimated to check the influence of unbalanced data. Further, 10 × 10 cross-validation paired t -test was utilized to test the best model with unseen data. Random forest was elected as the best model, differentiating the SARS-CoV-2 coronavirus from other coronaviruses and a control a group with an accuracy of 97.4 %, sensitivity of 96.2 %, and specificity of 98.2 %, when tested with unseen samples. Moreover, the proposed algorithm was computationally efficient, taking only 0.31 s to compute the genome biomarkers, outperforming previous studies. Graphical abstract: Image 1 Highlights: The article presents new biomarkers for the classification of SARS-CoV-2 / Non-SARS-CoV-2. The article presents a signal processing algorithm for automatic extraction of biomarkers based on three-base periodicity. The proposed biomarkers can differentiate SARS-CoV-2 from Non-SARS-CoV-2 and a control group with an accuracy of 97.4% . The proposed algorithm was computationally efficient, taking only 0.31 seconds .