Convolutional Neural Network Applied to SARS-CoV-2 Sequence Classification

Abstract

COVID-19, the illness caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus belonging to the Coronaviridade family, a single-strand positive-sense RNA genome, has been spreading around the world and has been declared a pandemic by the World Health Organization. On 17 January 2022, there were more than 329 million cases, with more than 5.5 million deaths. Although COVID-19 has a low mortality rate, its high capacities for contamination, spread, and mutation worry the authorities, especially after the emergence of the Omicron variant, which has a high transmission capacity and can more easily contaminate even vaccinated people. Such outbreaks require elucidation of the taxonomic classification and origin of the virus (SARS-CoV-2) from the genomic sequence for strategic planning, containment, and treatment of the disease. Thus, this work proposes a high-accuracy technique to classify viruses and other organisms from a genome sequence using a deep learning convolutional neural network (CNN). Unlike the other literature, the proposed approach does not limit the length of the genome sequence. The results show that the novel proposal accurately distinguishes SARS-CoV-2 from the sequences of other viruses. The results were obtained from 1557 instances of SARS-CoV-2 from the National Center for Biotechnology Information (NCBI) and 14,684 different viruses from the Virus-Host DB. As a CNN has several changeable parameters, the tests were performed with forty-eight different architectures; the best of these had an accuracy of 91.94 ± 2.62% in classifying viruses into their realms correctly, in addition to 100% accuracy in classifying SARS-CoV-2 into its respective realm, Riboviria. For the subsequent classifications (family, genera, and subgenus), this accuracy increased, which shows that the proposed architecture may be viable in the classification of the virus that causes COVID-19.

Machine Learning Algorithms Associate Case Numbers with SARS-CoV-2 Variants Rather Than with Impactful Mutations

“…During the COVID-19 pandemic, a substantial effort has been made to develop ML models to either predict case numbers from epidemiological data [ 3 , 4 , 5 , 6 , 7 ] or to classify SARS-CoV-2 sequences using genomic data [ 28 , 29 , 30 , 31 ], but in most situations, there was no attempt to explain the output of these models. With recent progress in model explainability, ML models are less and less considered as black boxes, and explaining them is especially important in epidemiology and biology.…”

DASH-CAM: Dynamic Approximate SearcH Content Addressable Memory for genome classification

“…Recently, convolutional neural network based classification solutions that differentiate between coronavirus (SARS-CoV-2) and other organisms have been proposed [1, 9].…”