10.25673/122857">


Proceedings of International Conference on Applied Innovation in IT
2025/12/22, Volume 13, Issue 5, pp.233-241

Efficient DNA Mutation Classification Using Deep Active Learning Techniques


Rana H. Saloom, Hussein K. Khafaji, Nabaa Ali Abd Alradha Al-saeedi, Saif Ali Abd Alradha Alsaidi and Furat Al Daamee


Abstract: Deep neural networks have shown impressive performance in a variety of fields, but the challenge in the training procedure for these networks may be time-consuming, particularly when working with huge datasets. Active learning is a potentially useful method that may speed up the training process by prioritizing the selection of samples that include the most relevant information for annotation. In this study, we propose an architecture for deep neural networks that combines active learning with Long Short-Term Memory (LSTM) in order to improve the effectiveness of the training process for DNA mutation categorization which is our contribution. We test the suggested method on the Cancer Cell Lines Encyclopedia (CCLE) dataset and assess its performance in comparison to a deep network and an LSTM network that do not use active learning. According to our experiments, the proposed approach is more accurate and improves efficiency by significantly reducing training time compared to other methods. When comparing deep learning models with deep active models, we found that the former averages a much higher training time of 4859.678 seconds while the latter averaged a substantially lower first training epoch of 978.8522 seconds, making total prediction and training time approximately 1474.385 seconds for the entire first phase. Focusing on some aspects outside of model creation as well, our research demonstrates the promise active learning holds in speeding up the training process for deep networks in context of classifying Deoxyribonucleic acid DNA mutations and emphasizes important aspects needed in constructing effective deep learning models.

Keywords: Mutations, variants, active learning, genetic classification, DNA.

DOI: 10.25673/122857

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References:

  1. 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.
  2. K. Korfmann, O. E. Gaggiotti, and M. Fumagalli, “Deep learning in population genetics,” Genome Biology and Evolution, vol. 15, no. 2, p. evad008, 2023.
  3. W. S. Alharbi and M. Rashid, “A review of deep learning applications in human genomics using next-generation sequencing data,” Human Genomics, vol. 16, no. 1, pp. 1-20, 2022.
  4. F. Alharbi and A. Vakanski, “Machine learning methods for cancer classification using gene expression data: A review,” Bioengineering, vol. 10, no. 2, p. 173, 2023.
  5. M. Taha and S. Alsaidi, “A review of classification techniques and computer-aided methods used for breast cancer detection.”
  6. M. A. Taha, S. A. A. A. Alsaidi, and R. A. Hussein, “Machine learning techniques for predicting heart diseases,” in 2022 International Symposium on Innovative Informatics of Biskra (ISNIB), 2022, pp. 1-6.
  7. H. Bi, M. Perello-Nieto, R. Santos-Rodriguez, P. Flach, and I. Craddock, “An active semi-supervised deep learning model for human activity recognition,” Journal of Ambient Intelligence and Humanized Computing, pp. 1-17, 2022.
  8. V. Khomenko, O. Shyshkov, O. Radyvonenko, and K. Bokhan, “Accelerating recurrent neural network training using sequence bucketing and multi-GPU data parallelization,” in 2016 IEEE First International Conference on Data Stream Mining & Processing (DSMP), IEEE, 2016, pp. 100-103.
  9. M. Wu, C. Li, and Z. Yao, “Deep active learning for computer vision tasks: Methodologies, applications, and challenges,” Applied Sciences, vol. 12, no. 16, p. 8103, 2022.
  10. X. An, M. Zhang, and S. Xu, “An active learning-based approach for screening scholarly articles about the origins of SARS-CoV-2,” PLOS ONE, vol. 17, no. 9, p. e0273725, 2022.
  11. G. Cong, G. Domeniconi, J. Shapiro, F. Zhou, and B. Chen, “Accelerating deep neural network training for action recognition on a cluster of GPUs,” in 2018 30th International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD), IEEE, 2018, pp. 298-305.
  12. K. Seo, S. Dodson, N. M. Harandi, N. Roberson, S. Fels, and I. Roll, “Active learning with online video: The impact of learning context on engagement,” Computers & Education, vol. 165, p. 104132, 2021.
  13. L. Huang, S. Matwin, E. J. de Carvalho, and R. Minghim, “Active learning with visualization for text data,” in Proceedings of the 2017 ACM Workshop on Exploratory Search and Interactive Data Analytics, 2017, pp. 69-74.
  14. S. Sapna, N. S. Sreenivasalu, and K. Paul, “DAPP: Accelerating training of DNN,” in 2018 IEEE 20th International Conference on High Performance Computing and Communications; IEEE 16th International Conference on Smart City; IEEE 4th International Conference on Data Science and Systems (HPCC/SmartCity/DSS), IEEE, 2018, pp. 867-872.
  15. C. Limberg, K. Krieger, H. Wersing, and H. Ritter, “Active learning for image recognition using a visualization-based user interface,” in Artificial Neural Networks and Machine Learning–ICANN 2019: Deep Learning, Springer, 2019, pp. 495-506.
  16. N. Yang, H. Tang, J. Yue, X. Yang, and Z. Xu, “Accelerating the training process of convolutional neural networks for classification by dropping training samples out,” in IGARSS 2018 IEEE International Geoscience and Remote Sensing Symposium, IEEE, 2018, pp. 7141-7144.
  17. P. Kumar and A. Gupta, “Active learning query strategies for classification, regression, and clustering: A survey,” Journal of Computer Science and Technology, vol. 35, pp. 913-945, 2020.
  18. C. C. Yang and G. Cong, “Accelerating data loading in deep neural network training,” in 2019 IEEE 26th International Conference on High Performance Computing, Data, and Analytics (HiPC), IEEE, 2019, pp. 235-245.
  19. V. Konkol’ová and J. Paralič, “Active learning in data science education,” in 2018 16th International Conference on Emerging eLearning Technologies and Applications (ICETA), IEEE, 2018, pp. 285-290.
  20. S. Kee, E. Del Castillo, and G. Runger, “Query-by-committee improvement with diversity and density in batch active learning,” Information Sciences, vol. 454, pp. 401-418, 2018.
  21. A. Borodzhieva, I. Tsvetkova, S. Zaharieva, D. Dimitrov, and V. Mutkov, “Active learning strategies applied for teaching the topic of registers,” in 2021 12th National Conference with International Participation (ELECTRONICA), IEEE, 2021, pp. 1-4.
  22. S. Schmidt, Q. Rao, J. Tatsch, and A. Knoll, “Advanced active learning strategies for object detection,” in 2020 IEEE Intelligent Vehicles Symposium (IV), IEEE, 2020, pp. 871-876.
  23. A. Hekimoglu, M. Schmidt, A. Marcos-Ramiro, and G. Rigoll, “Efficient active learning strategies for monocular 3D object detection,” in 2022 IEEE Intelligent Vehicles Symposium (IV), IEEE, 2022, pp. 295-302.
  24. S. Mohamadi and H. Amindavar, “Deep Bayesian active learning: A brief survey on recent advances,” arXiv preprint arXiv:2012.08044, 2020.
  25. S. Kee, E. del Castillo, and G. Runger, “Query-by-committee improvement with diversity and density in batch active learning,” Information Sciences, vol. 454-455, pp. 401-418, 2018, [Online]. Available: https://doi.org/10.1016/j.ins.2018.05.014.


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