Reference: | 1. Crick, F. H. (1958, January). On protein synthesis. In Symp Soc Exp Biol (Vol. 12, No. 138-63, p. 8). 2. Pribnow, D. (1975). Nucleotide sequence of an RNA polymerase binding site at an early T7 promoter. Proceedings of the National Academy of Sciences, 72(3), 784-788. 3. Myers, K. S., Noguera, D. R., & Donohue, T. J. (2021). Promoter architecture differences among alphaproteobacteria and other bacterial taxa. MSystems, 6(4), 10-1128. 4. Bhandari, N., Khare, S., Walambe, R., & Kotecha, K. (2021). Comparison of machine learning and deep learning techniques in promoter prediction across diverse species. PeerJ Computer Science, 7, e365. 5. Oubounyt, M., Louadi, Z., Tayara, H., & Chong, K. T. (2019). DeePromoter: robust promoter predictor using deep learning. Frontiers in genetics, 10, 286. 6. Zhang, M., Jia, C., Li, F., Li, C., Zhu, Y., Akutsu, T., ... & Song, J. (2022). Critical assessment of computational tools for prokaryotic and eukaryotic promoter prediction. Briefings in Bioinformatics, 23(2), bbab551. 7. Chevez-Guardado, R., & Peña-Castillo, L. (2021). Promotech: a general tool for bacterial promoter recognition. Genome Biology, 22, 1-16. 8. Ho, T. K. (1995, August). Random decision forests. In Proceedings of 3rd international conference on document analysis and recognition (Vol. 1, pp. 278-282). IEEE. 9. Dey, R., & Salem, F. M. (2017, August). Gate-variants of gated recurrent unit (GRU) neural networks. In 2017 IEEE 60th international midwest symposium on circuits and systems (MWSCAS) (pp. 1597-1600). IEEE. 10. Medsker, L. R., & Jain, L. (2001). Recurrent neural networks. Design and Applications, 5(64-67), 2. 11. Zhang, M., Li, F., Marquez-Lago, T. T., Leier, A., Fan, C., Kwoh, C. K., ... & Jia, C. (2019). MULTiPly: a novel multi-layer predictor for discovering general and specific types of promoters. Bioinformatics, 35(17), 2957-2965. 12. Rahman, M. S., Aktar, U., Jani, M. R., & Shatabda, S. (2019). iPro70-FMWin: identifying Sigma70 promoters using multiple windowing and minimal features. Molecular Genetics and Genomics, 294(1), 69-84. 13. Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural computation, 9(8), 1735-1780. 14. Kari, H., Bandi, S. M. S., Kumar, A., & Yella, V. R. (2022). Deepromclass: Delineator for eukaryotic core promoters employing deep neural networks. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 20(1), 802-807. 15. Martinez, G. S., Perez-Rueda, E., Kumar, A., Dutt, M., Maya, C. R., Ledesma-Dominguez, L., ... & Kelvin, D. J. (2024). CDBProm: the Comprehensive Directory of Bacterial Promoters. NAR Genomics and Bioinformatics, 6(1), lqae018. 16. Kuo, Syue-Ting (2023) High-Throughput Approaches Quantitatively Elucidate the Design Principles of Bacterial Regulatory Elements, National Taiwan University, Department of Life Science, Doctoral Dissertation 17. scanning model, May 2024, https://github.com/vickykao17/GBactPro/tree/main/scanning_model 18. Quinlan, A. R., & Hall, I. M. (2010). BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics, 26(6), 841-842. 19. Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., ... & Duchesnay, É. (2011). Scikit-learn: Machine learning in Python. the Journal of machine Learning research, 12, 2825-2830. 20. Coleman, G. A., Davín, A. A., Mahendrarajah, T. A., Szánthó, L. L., Spang, A., Hugenholtz, P., ... & Williams, T. A. (2021). A rooted phylogeny resolves early bacterial evolution. Science, 372(6542), eabe0511. |