DeepBiG: A Hybrid Supervised CNN and Bidirectional GRU Model for Predicting the DNA Sequence

Understanding the deoxyribonucleic acid (DNA) sequence is a major component of bioinformatics research. The amount of biological data increases tremendously. Hence, there is a need for effective approaches to handle the critical problem in the general computational framework of DNA sequence pre-diction and classification. Numerous deep learning languages can be used to complete these tasks compared to manual techniques that have been followed for ages. The aim of this project is to employ effective approaches for pre-processing DNA sequences and using deep learning languages to train the sequences for making judgments, predictions, and classifications of DNA se-quences into known categories. In this study, the pre-processing methods include k-mers and tokenization. We employ a novel hybrid deep learning algorithm that combines convolutional neural networks and is followed by bidirectional gated recurrent networks. This combination can capture dependencies within the genome sequence, even in large datasets with a lot of noise. The proposed model is compared with existing widely used models and classifiers. The results show that the proposed model achieves a good result with an accuracy of 82.90%. The dataset consists of 44,391 labeled DNA sequences obtained from the Encode project.

@article{Chuah2024,
  title     = {DeepBiG: A Hybrid Supervised CNN and Bidirectional GRU Model for Predicting the DNA Sequence},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {2},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Chai Wen Chuah and Wanxian He and De-Shuang Huang},
  doi       = {10.14569/IJACSA.2024.0150240},
  url       = {https://doi.org/10.14569/IJACSA.2024.0150240}
}