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Precision gene editing using deep learning: A case study of the CRISPR-Cas9 editor
- 1 Software Engineering,NortheasternUniversity
- 2 Software Engineering, Sun Yat-sen University
- 3 InformationStudies,Trine University
- 4 Information Studies, Trine University
- 5 Mathematics,Southern Methodist University
* Author to whom correspondence should be addressed.
Abstract
This article reviews the application cases of CRISPR/Cas9 gene editing technology, as well as the challenges and limitations. Firstly, the application of CRISPR/Cas9 technology based on deep learning in predicting the targeting efficiency of sgRNA is introduced, and the steps of data acquisition, pre-processing and feature engineering are described in detail. It then discusses the non-specific cutting and cytotoxicity challenges of CRISPR/Cas9 technology, as well as strategies for solving these challenges using deep learning techniques. Finally, the paper emphasizes the importance of deep learning techniques to mitigate the cytotoxicity problems in CRISPR/Cas9 technology, and points out that the establishment of these models can improve the safety and efficiency of gene editing experiments, and provide important reference and guidance for research in related fields.
Keywords
Gene editing technology, Deep learning, CRISPR/Cas9 technology, Prediction of sgRNA targeting, Gene ethics
[1]. A Programmable Dual-RNA–Guided DNA Endonuclease in Adaptive Bacterial Immunity. https://doi.org/10.1126/science.1225829
[2]. K. Xu, X. Wang, Z. Hu and Z. Zhang, "3D Face Recognition Based on Twin Neural Network Combining Deep Map and Texture," 2019 IEEE 19th International Conference on Communication Technology (ICCT), Xi'an, China, 2019, pp. 1665-1668, doi: 10.1109/ICCT46805.2019.8947113.
[3]. Nobel Prize 2020 in Chemistry honors CRISPR: a tool for rewriting the code of life.
[4]. Zheng, Jiajian, et al. "The Random Forest Model for Analyzing and Forecasting the US Stock Market in the Context of Smart Finance." arXiv preprint arXiv:2402.17194 (2024).
[5]. Yang, Le, et al. "AI-Driven Anonymization: Protecting Personal Data Privacy While Leveraging Machine Learning." arXiv preprint arXiv:2402.17191 (2024).
[6]. Yang Yang, WANG Fenglin, Liu De et al. Research progress of CRISPR⁃Cas9 technology in Production of plant secondary Metabolites [J]. Advances in Biotechnology, 2002,12(06):806-816.
[7]. Jiang, Fuguo, and Jennifer A. Doudna. "CRISPR–Cas9 structures and mechanisms." Annual review of biophysics 46 (2017): 505-529.
[8]. Redman, Melody, et al. "What is CRISPR/Cas9?." Archives of Disease in Childhood-Education and Practice 101.4 (2016): 213-215.
[9]. Zhu, Mengran, et al. "Utilizing GANs for Fraud Detection: Model Training with Synthetic Transaction Data." arXiv preprint arXiv:2402.09830 (2024).
[10]. Wu, Jiang, et al. "Data Pipeline Training: Integrating AutoML to Optimize the Data Flow of Machine Learning Models." arXiv preprint arXiv:2402.12916 (2024).
[11]. Yu, Hanyi, et al. "Machine Learning-Based Vehicle Intention Trajectory Recognition and Prediction for Autonomous Driving." arXiv preprint arXiv:2402.16036 (2024).
[12]. Huo, Shuning, et al. "Deep Learning Approaches for Improving Question Answering Systems in Hepatocellular Carcinoma Research." arXiv preprint arXiv:2402.16038 (2024).
[13]. Ma, Yuanwu, Lianfeng Zhang, and Xingxu Huang. "Genome modification by CRISPR/Cas9." The FEBS journal 281.23 (2014): 5186-5193.
[14]. Zhang, Jian-Hua, et al. "Optimization of genome editing through CRISPR-Cas9 engineering." Bioengineered 7.3 (2016): 166-174.
Cite this article
Cui,Z.;Lin,L.;Zong,Y.;Chen,Y.;Wang,S. (2024). Precision gene editing using deep learning: A case study of the CRISPR-Cas9 editor. Applied and Computational Engineering,64,133-140.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of the 6th International Conference on Computing and Data Science
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