OPTIMIZING VIRAL DNA SEQUENCE CLASSIFICATION WITH DEEP LEARNING AND GENETIC ALGORITHMS
Keywords:
Deep learning, viral genome classification, convolutional neural networks, genetic algorithm, DNA sequence encoding.Abstract
DNA sequence classification plays a vital role in biological data analysis, especially in identifying and
categorizing novel viral genomes. Accurate classification of these sequences is essential for mitigating the risks
of viral outbreaks, such as COVID-19, by expediting vaccine development. This study introduces a hybrid deep
learning model designed to improve the efficiency and accuracy of viral DNA sequence classification. The
proposed model combines Convolutional Neural Networks (CNN) with Long Short-Term Memory (LSTM) and
Bidirectional CNN-LSTM architectures. To further enhance performance, a Genetic Algorithm (GA) was
employed for optimizing the weights of the CNN. GA was selected due to its capability to navigate complex
search spaces effectively, boosting the model's feature extraction capabilities. Three encoding techniques were
investigated to transform DNA sequences into numerical formats suitable for model input: k-mer encoding, label
encoding, and one-hot vector encoding. Additionally, an advanced oversampling method was applied to address
the issue of imbalanced datasets. Among the tested configurations, the GA-optimized CNN hybrid model using
label encoding achieved the highest classification accuracy of 94.88%, outperforming other encoding methods.
