4e93f0bc-400a-4ab3-befd-a5ceca29d9f0 beie director@blueeyesintelligence.org 10.35940/ijitee.J9273.09111022 10.1109/FSCS.1990.89531M. Li, "Towards a DNA sequencing theory (learning a string)," Proceedings [1990] 31st Annual Symposium on Foundations of Computer Science, 1990, pp. 125-134 vol.1, doi: 10.1109/FSCS.1990.89531. [CrossRef]10.1109/ICASSP.2010.5495564T. Wu and H. Vikalo, "Maximum likelihood DNA sequence detection via sphere decoding," 2010 IEEE International Conference on Acoustics, Speech and Signal Processing, 2010, pp. 586-589, doi: 10.1109/ICASSP.2010.5495564. [CrossRef]10.1109/ICASSP.2006.1660522H. Eltoukhy and A. El Gamal, "Modeling and base-calling for Dna Sequencing-By-Synthesis," 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings, 2006, pp. II-II, doi: 10.1109/ICASSP.2006.1660522. [CrossRef]10.1109/10.784135S. W. Davies, M. Eizenman and S. Pasupathy, "Optimal structure for automatic processing of DNA sequences," in IEEE Transactions on Biomedical Engineering, vol. 46, no. 9, pp. 1044-1056, Sept. 1999, doi: 10.1109/10.784135. [CrossRef]10.1177/1094342016654214Memeti S, Pllana S. A machine learning approach for accelerating DNA sequence analysis. The International Journal of High Performance Computing Applications. 2018; 32(3):363-379. Doi: 10.1177/1094342016654214 [CrossRef]10.1088/1742-6596/1869/1/012093Hadikurniawati, W., Anwar, M. T., Marlina, D., & Kusumo, H. (2021, April). Predicting tuberculosis drug resistance using machine learning based on DNA sequencing data. In Journal of Physics: Conference Series (Vol. 1869, No. 1, p. 012093). IOP Publishing. [CrossRef]BMC cancerCario2011202010.1186/s12885-020-07318-xA machine learning approach to optimizing cell-free DNA sequencing panels: with an application to prostate cancerCario, C. L., Chen, E., Leong, L., Emami, N. C., Lopez, K., Tenggara, I., & Witte, J. S. (2020). A machine learning approach to optimizing cell-free DNA sequencing panels: with an application to prostate cancer. BMC cancer, 20(1), 1-9. [CrossRef]10.1038/s41598-021-89904-yZhang, Z., van Dijk, F., de Klein, N. et al. Feasibility of predicting allele specific expression from DNA sequencing using machine learning. Sci Rep 11, 10606 (2021). https://doi.org/10.1038/s41598-021-89904-y. [CrossRef]10.1093/bib/bbx165Bin Liu, BioSeq-Analysis: a platform for DNA, RNA and protein sequence analysis based on machine learning approaches, Briefings in Bioinformatics, Volume 20, Issue 4, July 2019, Pages 1280-1294, https://doi.org/10.1093/bib/bbx165. [CrossRef]10.1093/nar/gkz740Bin Liu, Xin Gao, Hanyu Zhang, BioSeq-Analysis2.0: an updated platform for analyzing DNA, RNA and protein sequences at sequence level and residue levelbased on machine learning approaches, Nucleic Acids Research, Volume 47, Issue 20, 18 November 2019, Page e127, https://doi.org/10.1093/nar/gkz740. [CrossRef]10.1109/ACCESS.2020.3003785F. Khan, C. Ncube, L. K. Ramasamy, S. Kadry and Y. Nam, "A Digital DNA Sequencing Engine for Ransomware Detection Using Machine Learning," in IEEE Access, vol. 8, pp. 119710-119719, 2020, doi: 10.1109/ACCESS.2020.3003785. [CrossRef]10.1109/ACCT.2015.73P. Dixit and G. I. Prajapati, "Machine Learning in Bioinformatics: A Novel Approach for DNA Sequencing," 2015 Fifth International Conference on Advanced Computing & Communication Technologies, 2015, pp. 41-47, doi: 10.1109/ACCT.2015.73. [CrossRef]10.1186/s12864-021-07841-6Wang, Y., Alangari, M., Hihath, J. et al. A machine learning approach for accurate and real-time DNA sequence identification. BMC Genomics 22, 525 (2021). [CrossRef]10.3389/fbioe.2020.01032Yang, A., Zhang, W., Wang, J., Yang, K., Han, Y. and Zhang, L., 2020. Review on the Application of Machine Learning Algorithms in the Sequence Data Mining of DNA. Frontiers in Bioengineering and Biotechnology, 8, p.1032. [CrossRef]10.1155/2021/1835056Gunasekaran, H., Ramalakshmi, K., Rex Macedo Arokiaraj, A., Deepa Kanmani, S., Venkatesan, C. and Suresh Gnana Dhas, C., 2021. Analysis of DNA Sequence Classification Using CNN and Hybrid Models. Computational and Mathematical Methods in Medicine, 2021. [CrossRef]