Utilizing chaos game representation for enhanced classification of SARS-CoV-2 variants with stacked sparse autoencoders

Since the beginning of the COVID-19 pandemic, the World Health Organization (WHO) has been tracking SARS-CoV-2 mutations. The SARS-CoV-2 consistently mutated throughout the pandemic, which resulted in many variants. A variant is a viral genome containing one or more genetic code mutations. Deep lear...

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Bibliographic Details
Published in:Neural computing & applications 2024-11, Vol.36 (31), p.19823-19837
Main Authors: Coutinho, Maria G. F., Câmara, Gabriel B. M., Barbosa, Raquel de M., Fernandes, Marcelo A. C.
Format: Article
Language:English
Subjects:
Artificial Intelligence
Artificial neural networks
Classification
Computational Biology/Bioinformatics
Computational Science and Engineering
Computer Science
Data Mining and Knowledge Discovery
Deep learning
Gene sequencing
Genetic code
Genomes
Image Processing and Computer Vision
Machine learning
Mutation
Original Article
Pandemics
Probability and Statistics in Computer Science
Representations
Severe acute respiratory syndrome coronavirus 2
Viral diseases
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Summary:Since the beginning of the COVID-19 pandemic, the World Health Organization (WHO) has been tracking SARS-CoV-2 mutations. The SARS-CoV-2 consistently mutated throughout the pandemic, which resulted in many variants. A variant is a viral genome containing one or more genetic code mutations. Deep learning techniques have been successfully used in many viral classification problems associated with viral infection diagnosis, metagenomics, phylogenetics, and analysis. This work proposed an effective viral genome classifier for SARS-CoV-2 variants using the deep neural network based on the stacked sparse autoencoder (SSAE). Aiming to achieve the best performance of the model, we explored the utilization of image representations of the complete genome sequences as the SSAE input. The dataset based on Chaos Game Representation (CGR) images was generated and applied to the experiments of classification of SARS-CoV-2 variants of concern (VOC). The SSAE technique provided great performance results, achieving classification accuracy of 99.9% for the validation set and 99.8% for the test set. Finally, the results indicated the relevance of using this deep learning technique in genome classification problems.
ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-024-10278-z