Authors - Elias Tabane, Zenghui Wang, Ernest Mnkandla Abstract - In this paper, we present a comprehensive analysis of ensemble deep learning models for DNA sequence classification. We explore the performance of three standalone models: Convolutional Neural Networks (CNN), Bidirectional Long Short-Term Memory (BiLSTM), and Gated Recurrent Units (GRU), along with an ensemble approach that combines all three. Our study evaluates the models based on four performance metrics: accuracy, precision, recall, and F1 score. The ensemble model achieved an accuracy of 90.6%, with precision, recall, and F1 score all at 0.91. We compare these results to the standalone models and demonstrate that ensemble learning significantly improves classification performance in the context of DNA sequence data. Additionally, we review relevant studies that have applied deep learning models to similar tasks and discuss the advantages of combining CNN, BiLSTM, and GRU for sequence classification tasks.
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