Pattern Recognition of Core-Promoter DNA Sequences Based on Recurrent Neural Network

Ahmed Oday, Shaimaa Khalid Moufak, Khadija Abbas Sahan, Jafar Mohammed and Mariam Maan Alzaak

References:

1. Y. He, Z. Shen, Q. Zhang, S. Wang, and D.-S. Huang, “A survey on deep learning in DNA/RNA motif mining,” Briefings in Bioinformatics, vol. 22, no. 4, Jul. 2021, doi: 10.1093/bib/bbaa229.
2. A. Busia et al., “A deep learning approach to pattern recognition for short DNA sequences,” bioRxiv, 2019, doi: 10.1101/353474.
3. V. X. Jin, G. A. C. Singer, F. J. Agosto-Pérez, S. Liyanarachchi, and R. V. Davuluri, “Genome-wide analysis of core promoter elements from conserved human and mouse orthologous pairs,” BMC Bioinformatics, vol. 7, p. 114, Mar. 2006, doi: 10.1186/1471-2105-7-114.
4. S. Minaee, Y. Y. Boykov, F. Porikli, A. J. Plaza, N. Kehtarnavaz, and D. Terzopoulos, “Image segmentation using deep learning: A survey,” IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 1–22, 2021, doi: 10.1109/TPAMI.2021.3059968.
5. J. M. Zhang, “Learning the language of the genome using RNNs,” 2016. [Online]. Available: https://api.semanticscholar.org/CorpusID:27419365.
6. T. Shafee and R. Lowe, “Eukaryotic and prokaryotic gene structure,” WikiJournal of Medicine, vol. 4, no. 1, pp. 2–6, 2017, doi: 10.15347/wjm/2017.002.
7. Y. Hara and T. Kawano, “Run-length encoding graphic rules applied to DNA-coded images and animation editable by polymerase chain reactions,” Journal of Advanced Computational Intelligence and Intelligent Informatics, vol. 19, no. 1, pp. 5–10, 2015, doi: 10.20965/jaciii.2015.p0005.
8. N. I. Gershenzon and I. P. Ioshikhes, “Synergy of human Pol II core promoter elements revealed by statistical sequence analysis,” Bioinformatics, vol. 21, no. 8, pp. 1295–1300, Apr. 2005, doi: 10.1093/bioinformatics/bti172.
9. S. K. Sønderby, C. K. Sønderby, L. Maaløe, and O. Winther, “Recurrent spatial transformer networks,” arXiv preprint arXiv:1506.02025, 2015.
10. I. H. Sarker, “Deep learning: A comprehensive overview on techniques, taxonomy, applications and research directions,” SN Computer Science, vol. 2, no. 6, p. 420, 2021, doi: 10.1007/s42979-021-00815-1.
11. S. Bustin, “Molecular Biology of the Cell, Sixth Edition; ISBN: 9780815344643; and Molecular Biology of the Cell, Sixth Edition, The Problems Book; ISBN 9780815344537,” International Journal of Molecular Sciences, vol. 16, no. 12, pp. 28123–28125, Dec. 2015, doi: 10.3390/ijms161226074.
12. F. Shiri, T. Perumal, N. Mustapha, and R. Mohamed, “A comprehensive overview and comparative analysis on deep learning models: CNN, RNN, LSTM, GRU,” arXiv preprint arXiv:2305.17473, 2023. [Online]. Available: https://api.semanticscholar.org/CorpusID:258960275.
13. A. A. K. Nielsen and C. A. Voigt, “Deep learning to predict the lab-of-origin of engineered DNA,” Nature Communications, vol. 9, no. 1, p. 3135, 2018, doi: 10.1038/s41467-018-05378-z.
14. A. Shrestha and A. Mahmood, “Review of deep learning algorithms and architectures,” IEEE Access, vol. 7, pp. 53040–53065, 2019, doi: 10.1109/ACCESS.2019.2912200.
15. J. Xiao and Z. Zhou, “Research progress of RNN language model,” in Proc. 2020 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA), 2020, pp. 1285–1288, doi: 10.1109/ICAICA50127.2020.9182390.
16. H. Apaydin et al., “Comparative analysis of recurrent neural network architectures for reservoir inflow forecasting,” Water, vol. 12, no. 5, 2020, doi: 10.3390/w12051500.
17. H. Yin et al., “Rainfall-runoff modeling using long short-term memory based step-sequence framework,” Journal of Hydrology, vol. 610, p. 127901, 2022, doi: 10.1016/j.jhydrol.2022.127901.
18. A. Mathew, P. Amudha, and S. Sivakumari, “Deep learning techniques: An overview,” in Advances in Machine Learning Technologies and Applications, A. E. Hassanien, R. Bhatnagar, and A. Darwish, Eds. Singapore: Springer, 2021, pp. 599–608.
19. L. Alzubaidi et al., “Review of deep learning: concepts, CNN architectures, challenges, applications, future directions,” Journal of Big Data, vol. 8, no. 1, p. 53, 2021, doi: 10.1186/s40537-021-00444-8.
20. DNA Promoter Region Analyzer. [Online]. Available: https://github.com/jafer0028/DNA-Promoter-Region-Analyzer. Accessed: Jun. 17, 2025.