A deep bidirectional recurrent neural network for identification of SARS-CoV-2 from viral genome sequences

In this work, Deep Bidirectional Recurrent Neural Networks (BRNNs) models were implemented based on both Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) cells in order to distinguish between genome sequence of SARS-CoV-2 and other Corona Virus strains such as SARS-CoV and MERS-CoV, Comm...

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Autores principales:	Mohanad A. Deif, Ahmed A. A. Solyman, Mehrdad Ahmadi Kamarposhti, Shahab S. Band, Rania E. Hammam
Formato:	article
Lenguaje:	EN
Publicado:	AIMS Press 2021
Materias:	recurrent neural networks deep learning covid-19 coronavirus sars-cov-2 gru lstm multi-class classification Biotechnology TP248.13-248.65 Mathematics QA1-939
Acceso en línea:	https://doaj.org/article/e55ce32d10b740d4a30e6d7e3bf5a80c
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spelling	oai:doaj.org-article:e55ce32d10b740d4a30e6d7e3bf5a80c2021-11-29T02:48:04ZA deep bidirectional recurrent neural network for identification of SARS-CoV-2 from viral genome sequences10.3934/mbe.20214401551-0018https://doaj.org/article/e55ce32d10b740d4a30e6d7e3bf5a80c2021-10-01T00:00:00Zhttps://www.aimspress.com/article/doi/10.3934/mbe.2021440?viewType=HTMLhttps://doaj.org/toc/1551-0018In this work, Deep Bidirectional Recurrent Neural Networks (BRNNs) models were implemented based on both Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) cells in order to distinguish between genome sequence of SARS-CoV-2 and other Corona Virus strains such as SARS-CoV and MERS-CoV, Common Cold and other Acute Respiratory Infection (ARI) viruses. An investigation of the hyper-parameters including the optimizer type and the number of unit cells, was also performed to attain the best performance of the BRNN models. Results showed that the GRU BRNNs model was able to discriminate between SARS-CoV-2 and other classes of viruses with a higher overall classification accuracy of 96.8% as compared to that of the LSTM BRNNs model having a 95.8% overall classification accuracy. The best hyper-parameters producing the highest performance for both models was obtained when applying the SGD optimizer and an optimum number of unit cells of 80 in both models. This study proved that the proposed GRU BRNN model has a better classification ability for SARS-CoV-2 thus providing an efficient tool to help in containing the disease and achieving better clinical decisions with high precision.Mohanad A. DeifAhmed A. A. Solyman Mehrdad Ahmadi Kamarposhti Shahab S. BandRania E. HammamAIMS Pressarticlerecurrent neural networksdeep learningcovid-19coronavirussars-cov-2grulstm multi-class classificationBiotechnologyTP248.13-248.65MathematicsQA1-939ENMathematical Biosciences and Engineering, Vol 18, Iss 6, Pp 8933-8950 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	recurrent neural networks deep learning covid-19 coronavirus sars-cov-2 gru lstm multi-class classification Biotechnology TP248.13-248.65 Mathematics QA1-939
spellingShingle	recurrent neural networks deep learning covid-19 coronavirus sars-cov-2 gru lstm multi-class classification Biotechnology TP248.13-248.65 Mathematics QA1-939 Mohanad A. Deif Ahmed A. A. Solyman Mehrdad Ahmadi Kamarposhti Shahab S. Band Rania E. Hammam A deep bidirectional recurrent neural network for identification of SARS-CoV-2 from viral genome sequences
description	In this work, Deep Bidirectional Recurrent Neural Networks (BRNNs) models were implemented based on both Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) cells in order to distinguish between genome sequence of SARS-CoV-2 and other Corona Virus strains such as SARS-CoV and MERS-CoV, Common Cold and other Acute Respiratory Infection (ARI) viruses. An investigation of the hyper-parameters including the optimizer type and the number of unit cells, was also performed to attain the best performance of the BRNN models. Results showed that the GRU BRNNs model was able to discriminate between SARS-CoV-2 and other classes of viruses with a higher overall classification accuracy of 96.8% as compared to that of the LSTM BRNNs model having a 95.8% overall classification accuracy. The best hyper-parameters producing the highest performance for both models was obtained when applying the SGD optimizer and an optimum number of unit cells of 80 in both models. This study proved that the proposed GRU BRNN model has a better classification ability for SARS-CoV-2 thus providing an efficient tool to help in containing the disease and achieving better clinical decisions with high precision.
format	article
author	Mohanad A. Deif Ahmed A. A. Solyman Mehrdad Ahmadi Kamarposhti Shahab S. Band Rania E. Hammam
author_facet	Mohanad A. Deif Ahmed A. A. Solyman Mehrdad Ahmadi Kamarposhti Shahab S. Band Rania E. Hammam
author_sort	Mohanad A. Deif
title	A deep bidirectional recurrent neural network for identification of SARS-CoV-2 from viral genome sequences
title_short	A deep bidirectional recurrent neural network for identification of SARS-CoV-2 from viral genome sequences
title_full	A deep bidirectional recurrent neural network for identification of SARS-CoV-2 from viral genome sequences
title_fullStr	A deep bidirectional recurrent neural network for identification of SARS-CoV-2 from viral genome sequences
title_full_unstemmed	A deep bidirectional recurrent neural network for identification of SARS-CoV-2 from viral genome sequences
title_sort	deep bidirectional recurrent neural network for identification of sars-cov-2 from viral genome sequences
publisher	AIMS Press
publishDate	2021
url	https://doaj.org/article/e55ce32d10b740d4a30e6d7e3bf5a80c
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A deep bidirectional recurrent neural network for identification of SARS-CoV-2 from viral genome sequences

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