农业大数据学报

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深度学习在作物基因编辑技术的应用与研究进展

赵晓燕1,2,4 ，周焕斌2,3，周国民2,4,5,6*，张建华1,2,4*   

  1. 1.中国农业科学院农业信息研究所/农业农村部农业大数据重点实验室 北京 100081；2.三亚中国农业科学院国家南繁研究院，三亚 572024； 3中国农业科学院植物保护研究所，北京 100081；4. 国家农业科学数据中心，北京 100081；5.农业农村部南京农业机械化研究所，南京210014；6.中国农业科学院西部农业研究中心，新疆 831100
  • 发布日期:2025-12-28

Application of deep learning in crop gene editing technology and research progress

Zhao XiaoYan1,2,4, Zhou HuanBin2,3, Zhou GuoMin1,2,4,5*,Zhang JianHua1,2,4*   

  1. 1.Institute of Agricultural Information, Chinese Academy of Agricultural Sciences/Key Laboratory of Agricultural Big Data,Ministry of Agriculture and Rural Affairs, Beijing 100081, China; 2.Nation Nanfan Research Institute, Chinese Academy of Agricultural Sciences, Sanya 572024, China; 3. Institute of Plant Protection, Chinese Academy of Agricultural Sciences, Beijing 100081, China;4.National Agricultural Science Data Center, Beijing 100081, China;5.Nanjing Institute of Agricultural Mechanization, Ministry of Agriculture and Rural Affairs, Nanjing 210014, China;6.Institute of western Agriculture, Chinese Academy of Agricultural Sciences, Xinjiang 831100, China
  • Online:2025-12-28

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摘要： 近年来，基因编辑技术发展迅猛，已成为基础基因功能研究与生物育种的核心工具。并且随着计算机和大数据的增长推动了深度学习在基因编辑中的应用，深度学习技术在优化基因编辑过程，特别是在提升作物改良效率方面，正发挥着日益显著的作用。本文综述了深度学习在基因编辑优化方面的研究进展，重点介绍了深度学习在基因编辑效率和特异性增强方面的应用。此外，文章深入探讨了深度学习与基因编辑深度融合所面临的技术挑战，并展望了其未来发展前景。通过将先进的基因编辑技术与深度学习相结合，未来将进一步加快作物育种的进展。

关键词: 深度学习, 语言模型, 基因编辑

Abstract: In recent years, gene editing technology has developed rapidly and has become a core tool for basic gene function research and biological breeding. And with the growth of computers and big data driving the application of deep learning in gene editing, deep learning technology is playing an increasingly significant role in optimizing the gene editing process, especially in enhancing the efficiency of crop improvement. This article reviews the research progress of deep learning in gene editing optimization, focusing on the application of deep learning in gene editing efficiency and specificity enhancement. In addition, the article delves into the technical challenges facing the deep integration of deep learning and gene editing, and looks forward to its future development prospects. By combining advanced gene editing technology with deep learning, the progress of crop breeding will be further accelerated in the future.

Key words: Deep learning, Language Models, Gene Editing

主管：中华人民共和国农业农村部　　
主办：中国农业科学院农业信息研究所　　
ISSN 2096-6369　　CN 10-1555/G2
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