Convolutional Neural Network Applied to SARS-CoV-2 Sequence Classification
COVID-19, the illness caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus belonging to the <i>Coronaviridade</i> family, a single-strand positive-sense RNA genome, has been spreading around the world and has been declared a pandemic by the World Health Organi...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-07-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/15/5730 |
| _version_ | 1797412272245571584 |
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| author | Gabriel B. M. Câmara Maria G. F. Coutinho Lucileide M. D. da Silva Walter V. do N. Gadelha Matheus F. Torquato Raquel de M. Barbosa Marcelo A. C. Fernandes |
| author_facet | Gabriel B. M. Câmara Maria G. F. Coutinho Lucileide M. D. da Silva Walter V. do N. Gadelha Matheus F. Torquato Raquel de M. Barbosa Marcelo A. C. Fernandes |
| author_sort | Gabriel B. M. Câmara |
| collection | DOAJ |
| description | COVID-19, the illness caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus belonging to the <i>Coronaviridade</i> 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, <i>Riboviria</i>. 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. |
| first_indexed | 2024-03-09T05:00:38Z |
| format | Article |
| id | doaj.art-fe77f99658d540d18ab4b0e3748d7b98 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T05:00:38Z |
| publishDate | 2022-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-fe77f99658d540d18ab4b0e3748d7b982023-12-03T13:01:20ZengMDPI AGSensors1424-82202022-07-012215573010.3390/s22155730Convolutional Neural Network Applied to SARS-CoV-2 Sequence ClassificationGabriel B. M. Câmara0Maria G. F. Coutinho1Lucileide M. D. da Silva2Walter V. do N. Gadelha3Matheus F. Torquato4Raquel de M. Barbosa5Marcelo A. C. Fernandes6Bioinformatics Multidisciplinary Environment (BioME), Federal University of Rio Grande do Norte, Natal 59078-970, RN, BrazilLaboratory of Machine Learning and Intelligent Instrumentation, Federal University of Rio Grande do Norte, Natal 59078-970, RN, BrazilLaboratory of Machine Learning and Intelligent Instrumentation, Federal University of Rio Grande do Norte, Natal 59078-970, RN, BrazilLaboratory of Machine Learning and Intelligent Instrumentation, Federal University of Rio Grande do Norte, Natal 59078-970, RN, BrazilLaboratory of Machine Learning and Intelligent Instrumentation, Federal University of Rio Grande do Norte, Natal 59078-970, RN, BrazilLaboratory of Machine Learning and Intelligent Instrumentation, Federal University of Rio Grande do Norte, Natal 59078-970, RN, BrazilBioinformatics Multidisciplinary Environment (BioME), Federal University of Rio Grande do Norte, Natal 59078-970, RN, BrazilCOVID-19, the illness caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus belonging to the <i>Coronaviridade</i> 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, <i>Riboviria</i>. 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.https://www.mdpi.com/1424-8220/22/15/5730SARS-CoV-2COVID-19deep learningCNN |
| spellingShingle | Gabriel B. M. Câmara Maria G. F. Coutinho Lucileide M. D. da Silva Walter V. do N. Gadelha Matheus F. Torquato Raquel de M. Barbosa Marcelo A. C. Fernandes Convolutional Neural Network Applied to SARS-CoV-2 Sequence Classification Sensors SARS-CoV-2 COVID-19 deep learning CNN |
| title | Convolutional Neural Network Applied to SARS-CoV-2 Sequence Classification |
| title_full | Convolutional Neural Network Applied to SARS-CoV-2 Sequence Classification |
| title_fullStr | Convolutional Neural Network Applied to SARS-CoV-2 Sequence Classification |
| title_full_unstemmed | Convolutional Neural Network Applied to SARS-CoV-2 Sequence Classification |
| title_short | Convolutional Neural Network Applied to SARS-CoV-2 Sequence Classification |
| title_sort | convolutional neural network applied to sars cov 2 sequence classification |
| topic | SARS-CoV-2 COVID-19 deep learning CNN |
| url | https://www.mdpi.com/1424-8220/22/15/5730 |
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