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Multi-Stage Temporal Convolution Network for COVID-19 Variant Classification
The outbreak of the novel coronavirus disease COVID-19 (SARS-CoV-2) has developed into a global epidemic. Due to the pathogenic virus’s high transmission rate, accurate identification and early prediction are required for subsequent therapy. Moreover, the virus’s polymorphic nature allows it to evol...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9689740/ https://www.ncbi.nlm.nih.gov/pubmed/36359579 http://dx.doi.org/10.3390/diagnostics12112736 |
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author | Ullah, Waseem Ullah, Amin Malik, Khalid Mahmood Saudagar, Abdul Khader Jilani Khan, Muhammad Badruddin Hasanat, Mozaherul Hoque Abul AlTameem, Abdullah AlKhathami, Mohammed |
author_facet | Ullah, Waseem Ullah, Amin Malik, Khalid Mahmood Saudagar, Abdul Khader Jilani Khan, Muhammad Badruddin Hasanat, Mozaherul Hoque Abul AlTameem, Abdullah AlKhathami, Mohammed |
author_sort | Ullah, Waseem |
collection | PubMed |
description | The outbreak of the novel coronavirus disease COVID-19 (SARS-CoV-2) has developed into a global epidemic. Due to the pathogenic virus’s high transmission rate, accurate identification and early prediction are required for subsequent therapy. Moreover, the virus’s polymorphic nature allows it to evolve and adapt to various environments, making prediction difficult. However, other diseases, such as dengue, MERS-CoV, Ebola, SARS-CoV-1, and influenza, necessitate the employment of a predictor based on their genomic information. To alleviate the situation, we propose a deep learning-based mechanism for the classification of various SARS-CoV-2 virus variants, including the most recent, Omicron. Our model uses a neural network with a temporal convolution neural network to accurately identify different variants of COVID-19. The proposed model first encodes the sequences in the numerical descriptor, and then the convolution operation is applied for discriminative feature extraction from the encoded sequences. The sequential relations between the features are collected using a temporal convolution network to classify COVID-19 variants accurately. We collected recent data from the NCBI, on which the proposed method outperforms various baselines with a high margin. |
format | Online Article Text |
id | pubmed-9689740 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-96897402022-11-25 Multi-Stage Temporal Convolution Network for COVID-19 Variant Classification Ullah, Waseem Ullah, Amin Malik, Khalid Mahmood Saudagar, Abdul Khader Jilani Khan, Muhammad Badruddin Hasanat, Mozaherul Hoque Abul AlTameem, Abdullah AlKhathami, Mohammed Diagnostics (Basel) Article The outbreak of the novel coronavirus disease COVID-19 (SARS-CoV-2) has developed into a global epidemic. Due to the pathogenic virus’s high transmission rate, accurate identification and early prediction are required for subsequent therapy. Moreover, the virus’s polymorphic nature allows it to evolve and adapt to various environments, making prediction difficult. However, other diseases, such as dengue, MERS-CoV, Ebola, SARS-CoV-1, and influenza, necessitate the employment of a predictor based on their genomic information. To alleviate the situation, we propose a deep learning-based mechanism for the classification of various SARS-CoV-2 virus variants, including the most recent, Omicron. Our model uses a neural network with a temporal convolution neural network to accurately identify different variants of COVID-19. The proposed model first encodes the sequences in the numerical descriptor, and then the convolution operation is applied for discriminative feature extraction from the encoded sequences. The sequential relations between the features are collected using a temporal convolution network to classify COVID-19 variants accurately. We collected recent data from the NCBI, on which the proposed method outperforms various baselines with a high margin. MDPI 2022-11-09 /pmc/articles/PMC9689740/ /pubmed/36359579 http://dx.doi.org/10.3390/diagnostics12112736 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Ullah, Waseem Ullah, Amin Malik, Khalid Mahmood Saudagar, Abdul Khader Jilani Khan, Muhammad Badruddin Hasanat, Mozaherul Hoque Abul AlTameem, Abdullah AlKhathami, Mohammed Multi-Stage Temporal Convolution Network for COVID-19 Variant Classification |
title | Multi-Stage Temporal Convolution Network for COVID-19 Variant Classification |
title_full | Multi-Stage Temporal Convolution Network for COVID-19 Variant Classification |
title_fullStr | Multi-Stage Temporal Convolution Network for COVID-19 Variant Classification |
title_full_unstemmed | Multi-Stage Temporal Convolution Network for COVID-19 Variant Classification |
title_short | Multi-Stage Temporal Convolution Network for COVID-19 Variant Classification |
title_sort | multi-stage temporal convolution network for covid-19 variant classification |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9689740/ https://www.ncbi.nlm.nih.gov/pubmed/36359579 http://dx.doi.org/10.3390/diagnostics12112736 |
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