Attention-Based Hybrid Deep Learning Models for Classifying COVID-19 Genome Sequences
<b>Background:</b> COVID-19 genetic sequence research is crucial despite immunizations and pandemic control. COVID-19-causing SARS-CoV-2 must be understood genomically for several reasons. New viral strains may resist vaccines. Categorizing genetic sequences helps researchers track chang...
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MDPI AG
2025-01-01
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| author | A. M. Mutawa |
| author_facet | A. M. Mutawa |
| author_sort | A. M. Mutawa |
| collection | DOAJ |
| description | <b>Background:</b> COVID-19 genetic sequence research is crucial despite immunizations and pandemic control. COVID-19-causing SARS-CoV-2 must be understood genomically for several reasons. New viral strains may resist vaccines. Categorizing genetic sequences helps researchers track changes and assess immunization efficacy. Classifying COVID-19 genome sequences with other viruses helps to understand its evolution and interactions with other illnesses. <b>Methods</b>: The proposed study introduces a deep learning-based COVID-19 genomic sequence categorization approach. Attention-based hybrid deep learning (DL) models categorize 1423 COVID-19 and 11,388 other viral genome sequences. An unknown dataset is also used to assess the models. The five models’ accuracy, f1-score, area under the curve (AUC), precision, Matthews correlation coefficient (MCC), and recall are evaluated. <b>Results</b>: The results indicate that the Convolutional neural network (CNN) with Bidirectional long short-term memory (BLSTM) with attention layer (CNN-BLSTM-Att) achieved an accuracy of 99.99%, which outperformed the other models. For external validation, the model shows an accuracy of 99.88%. It reveals that DL-based approaches with an attention layer can accurately classify COVID-19 genomic sequences with a high degree of accuracy. This method might assist in identifying and classifying COVID-19 virus strains in clinical situations. Immunizations have lowered COVID-19 danger, but categorizing its genetic sequences is crucial to global health activities to plan for recurrence or future viral threats. |
| format | Article |
| id | doaj-art-d20a588e6ee74593b9ef78efbb02209f |
| institution | Kabale University |
| issn | 2673-2688 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
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| series | AI |
| spelling | doaj-art-d20a588e6ee74593b9ef78efbb02209f2025-01-24T13:17:21ZengMDPI AGAI2673-26882025-01-0161410.3390/ai6010004Attention-Based Hybrid Deep Learning Models for Classifying COVID-19 Genome SequencesA. M. Mutawa0Computer Engineering Department, College of Engineering and Petroleum, Kuwait University, Safat 13060, Kuwait<b>Background:</b> COVID-19 genetic sequence research is crucial despite immunizations and pandemic control. COVID-19-causing SARS-CoV-2 must be understood genomically for several reasons. New viral strains may resist vaccines. Categorizing genetic sequences helps researchers track changes and assess immunization efficacy. Classifying COVID-19 genome sequences with other viruses helps to understand its evolution and interactions with other illnesses. <b>Methods</b>: The proposed study introduces a deep learning-based COVID-19 genomic sequence categorization approach. Attention-based hybrid deep learning (DL) models categorize 1423 COVID-19 and 11,388 other viral genome sequences. An unknown dataset is also used to assess the models. The five models’ accuracy, f1-score, area under the curve (AUC), precision, Matthews correlation coefficient (MCC), and recall are evaluated. <b>Results</b>: The results indicate that the Convolutional neural network (CNN) with Bidirectional long short-term memory (BLSTM) with attention layer (CNN-BLSTM-Att) achieved an accuracy of 99.99%, which outperformed the other models. For external validation, the model shows an accuracy of 99.88%. It reveals that DL-based approaches with an attention layer can accurately classify COVID-19 genomic sequences with a high degree of accuracy. This method might assist in identifying and classifying COVID-19 virus strains in clinical situations. Immunizations have lowered COVID-19 danger, but categorizing its genetic sequences is crucial to global health activities to plan for recurrence or future viral threats.https://www.mdpi.com/2673-2688/6/1/4attention layerconvolutional neural networkCOVID-19deep learninggenome sequencingsequence classification |
| spellingShingle | A. M. Mutawa Attention-Based Hybrid Deep Learning Models for Classifying COVID-19 Genome Sequences AI attention layer convolutional neural network COVID-19 deep learning genome sequencing sequence classification |
| title | Attention-Based Hybrid Deep Learning Models for Classifying COVID-19 Genome Sequences |
| title_full | Attention-Based Hybrid Deep Learning Models for Classifying COVID-19 Genome Sequences |
| title_fullStr | Attention-Based Hybrid Deep Learning Models for Classifying COVID-19 Genome Sequences |
| title_full_unstemmed | Attention-Based Hybrid Deep Learning Models for Classifying COVID-19 Genome Sequences |
| title_short | Attention-Based Hybrid Deep Learning Models for Classifying COVID-19 Genome Sequences |
| title_sort | attention based hybrid deep learning models for classifying covid 19 genome sequences |
| topic | attention layer convolutional neural network COVID-19 deep learning genome sequencing sequence classification |
| url | https://www.mdpi.com/2673-2688/6/1/4 |
| work_keys_str_mv | AT ammutawa attentionbasedhybriddeeplearningmodelsforclassifyingcovid19genomesequences |