ArticleIEEE access : practical innovations, open solutions2021
Deep Learning for SARS COV-2 Genome Sequences.
Article in IEEE access : practical innovations, open solutions, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
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Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it.
- AI-driven techniques for detection and mitigation of SARS-CoV-2 spread: a review, taxonomy, and trends.Clinical and experimental medicine · 2025Pooled it
- Deciphering 6-mer Spectra Distribution Rules in Coronavirus Genomes: Application to Comparative Genomic Analysis.International journal of molecular sciences · 2026Article
- Pattern Recognition Algorithms in Pharmacogenomics and Drug Repurposing-Case Study: Ribavirin and Lopinavir.Pharmaceuticals (Basel, Switzerland) · 2025Review
- Artificial Intelligence in Infectious Disease Diagnostic Technologies.Diagnostics (Basel, Switzerland) · 2025Review
- A model for epileptic EEG detection and recognition based on Multi-Attention mechanism and Spatiotemporal.Scientific reports · 2025Article
- A privacy-preserving dependable deep federated learning model for identifying new infections from genome sequences.Scientific reports · 2025Article
- A hybrid CNN-Bi-LSTM model with feature fusion for accurate epilepsy seizure detection.BMC medical informatics and decision making · 2025Article
- Enhanced deep Convolutional Neural Network for SARS-CoV-2 variants classification.Frontiers in artificial intelligence · 2025Article
- Advancements in Viral Genomics: Gated Recurrent Unit Modeling of SARS-CoV-2, SARS, MERS, and Ebola viruses.Revista da Sociedade Brasileira de Medicina Tropical · 2025Article
- Deepvirusclassifier: a deep learning tool for classifying SARS-CoV-2 based on viral subtypes within the coronaviridae family.BMC bioinformatics · 2024Article
- DHFS-ECM: Design of a Dual Heuristic Feature Selection-based Ensemble Classification Model for the Identification of Bamboo Species from Genomic Sequences.Current genomics · 2024Article
- Analysis of Emerging Variants of Turkey Reovirus using Machine Learning.Briefings in bioinformatics · 2024Article
- A Study and Analysis of Disease Identification using Genomic Sequence Processing Models: An Empirical Review.Current genomics · 2023Review
- HostNet: improved sequence representation in deep neural networks for virus-host prediction.BMC bioinformatics · 2023Article
- Machine Learning Algorithms Associate Case Numbers with SARS-CoV-2 Variants Rather Than with Impactful Mutations.Viruses · 2023Article
- A new profiling approach for DNA sequences based on the nucleotides' physicochemical features for accurate analysis of SARS-CoV-2 genomes.BMC genomics · 2023Article
- Conventional and Novel Diagnostic Tools for the Diagnosis of Emerging SARS-CoV-2 Variants.Vaccines · 2023Review
- SARS-CoV-2 virus classification based on stacked sparse autoencoder.Computational and structural biotechnology journal · 2023Article
- Heuristic Analysis of Genomic Sequence Processing Models for High Efficiency Prediction: A Statistical Perspective.Current genomics · 2022Review
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The SARS-CoV-2 virus which originated in Wuhan, China has since spread throughout the world and is affecting millions of people. When there is a novel virus outbreak, it is crucial to quickly determine if the epidemic is a result of the novel virus or a well-known virus. We propose a deep learning algorithm that uses a convolutional neural network (CNN) as well as a bi-directional long short-term memory (Bi-LSTM) neural network, for the classification of the severe acute respiratory syndrome coronavirus 2 (SARS CoV-2) amongst Coronaviruses. Besides, we classify whether a genome sequence contains candidate regulatory motifs or otherwise. Regulatory motifs bind to transcription factors. Transcription factors are responsible for the expression of genes. The experimental results show that at peak performance, the proposed convolutional neural network bi-directional long short-term memory (CNN-Bi-LSTM) model achieves a classification accuracy of 99.95%, area under curve receiver operating characteristic (AUC ROC) of 100.00%, a specificity of 99.97%, the sensitivity of 99.97%, Cohen's Kappa equal to 0.9978, Mathews Correlation Coefficient (MCC) equal to 0.9978 for the classification of SARS CoV-2 amongst Coronaviruses. Also, the CNN-Bi-LSTM correctly detects whether a sequence has candidate regulatory motifs or binding-sites with a classification accuracy of 99.76%, AUC ROC of 100.00%, a specificity of 99.76%, a sensitivity of 99.76%, MCC equal to 0.9980, and Cohen's Kappa of 0.9970 at peak performance. These results are encouraging enough to recognise deep learning algorithms as alternative avenues for detecting SARS CoV-2 as well as detecting regulatory motifs in the SARS CoV-2 genes.
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