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

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dc.authoridhttps://orcid.org/0000-0002-2881-8635en_US
dc.authoridhttps://orcid.org/0000-0003-4581-1619en_US
dc.authoridhttps://orcid.org/0000-0001-6109-1311en_US
dc.authoridhttps://orcid.org/0000-0002-1460-3115en_US
dc.contributor.authorDeif, Mohanad A.
dc.contributor.authorSolyman, Ahmad Amin Ahmad
dc.contributor.authorKamarposhti, Mehrdad Ahmadi
dc.contributor.authorBand, Shahab S.
dc.contributor.authorHammam, Rania E.
dc.date.accessioned2023-05-11T14:53:55Z
dc.date.available2023-05-11T14:53:55Z
dc.date.issued2021en_US
dc.departmentMühendislik ve Mimarlık Fakültesien_US
dc.description.abstractIn 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 hyperparameters 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.en_US
dc.identifier.doi10.3934/mbe.2021440en_US
dc.identifier.endpage8950en_US
dc.identifier.issn1547-1063
dc.identifier.issn1551-0018
dc.identifier.issue6en_US
dc.identifier.pmid34814329en_US
dc.identifier.scopus2-s2.0-85117921115en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage8933en_US
dc.identifier.urihttps://hdl.handle.net/11363/4607
dc.identifier.urihttps://doi.org/
dc.identifier.volume18en_US
dc.identifier.wosWOS:000733406700012en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherAMER INST MATHEMATICAL SCIENCES-AIMS, PO BOX 2604, SPRINGFIELD, MO 65801-2604en_US
dc.relation.ispartofMathematical Biosciences and Engineeringen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/us/*
dc.subjectrecurrent neural networksen_US
dc.subjectdeep learningen_US
dc.subjectCOVID-19en_US
dc.subjectcoronavirusen_US
dc.subjectSARS-CoV-2en_US
dc.subjectGRUen_US
dc.subjectLSTM Multi-class classificationen_US
dc.titleA deep bidirectional recurrent neural network for identification of SARS-CoV-2 from viral genome sequencesen_US
dc.typeArticleen_US

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