Harnessing Ensemble Deep Learning for DNA Sequence Classification: Evaluating CNN, BiLSTM, and GRU Architectures

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 com-bines all three. Our study evaluates the models based on four performance metrics: accu-racy, 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 demon-strate that ensemble learning significantly improves classification performance in the context of DNA sequence data. Additionally, we review relevant stud-ies that have applied deep learning models to similar tasks and discuss the advantages of combining CNN, BiLSTM, and GRU for sequence classification tasks.