Sarab Almuhaideb1,*, Najwa Altwaijry1, Isra Al-Turaiki1, Ahmad Raza Khan2, Hamza Ali Rizvi3
CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 3095-3128, 2025, DOI:10.32604/cmc.2025.067390
- 23 September 2025
Abstract Many bioinformatics applications require determining the class of a newly sequenced Deoxyribonucleic acid (DNA) sequence, making DNA sequence classification an integral step in performing bioinformatics analysis, where large biomedical datasets are transformed into valuable knowledge. Existing methods rely on a feature extraction step and suffer from high computational time requirements. In contrast, newer approaches leveraging deep learning have shown significant promise in enhancing accuracy and efficiency. In this paper, we investigate the performance of various deep learning architectures: Convolutional Neural Network (CNN), CNN-Long Short-Term Memory (CNN-LSTM), CNN-Bidirectional Long Short-Term Memory (CNN-BiLSTM), Residual Network (ResNet), and… More >
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