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Article Details

Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

A Deep Learning Approach for Viral DNA Sequence Classification using Genetic Algorithm

Author 1: Ahmed El-Tohamy
Author 2: Huda Amin Maghwary
Author 3: Nagwa Badr

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Digital Object Identifier (DOI) : 10.14569/IJACSA.2022.0130861

Article Published in International Journal of Advanced Computer Science and Applications(IJACSA), Volume 13 Issue 8, 2022.

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Abstract: DNA sequence classification is one of the major challenges in biological data processing. The identification and classification of novel viral genome sequences drastically help in reducing the dangers of a viral outbreak like COVID-19. The more accurate the classification of these viruses, the faster a vaccine can be produced to counter them. Thus, more accurate methods should be utilized to classify the viral DNA. This research proposes a hybrid deep learning model for efficient viral DNA sequence classification. A genetic algorithm (GA) was utilized for weight optimization with Convolutional Neural Networks (CNN) architecture. Furthermore, Long Short-Term Memory (LSTM) as well as Bidirectional CNN-LSTM model architectures are employed. Encoding methods are needed to transform the DNA into numeric format for the proposed model. Three different encoding methods to represent DNA sequences as input to the proposed model were experimented: k-mer, label encoding, and one hot vector encoding. Furthermore, an efficient oversampling method was applied to overcome the imbalanced dataset issues. The performance of the proposed GA optimized CNN hybrid model using label encoding achieved the highest classification accuracy of 94.88% compared with other encoding methods.

Keywords: Deep learning; sequence classification; convolutional neural networks; genetic algorithm; sequence encoding

Ahmed El-Tohamy, Huda Amin Maghwary and Nagwa Badr, “A Deep Learning Approach for Viral DNA Sequence Classification using Genetic Algorithm” International Journal of Advanced Computer Science and Applications(IJACSA), 13(8), 2022. http://dx.doi.org/10.14569/IJACSA.2022.0130861

@article{El-Tohamy2022,
title = {A Deep Learning Approach for Viral DNA Sequence Classification using Genetic Algorithm},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2022.0130861},
url = {http://dx.doi.org/10.14569/IJACSA.2022.0130861},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {8},
author = {Ahmed El-Tohamy and Huda Amin Maghwary and Nagwa Badr}
}


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