数字农科院2.0

Innovative opportunities for gene editing technology in crop breeding: from the perspective of literature analysis

文献类型： 外文期刊

作者： Hui Zhang;Ru Yao;Qian Jia;Shijie Qi;Ying He;Jingjuan Zhao;Limin Chuan

作者机构：

关键词： crop breeding;gene editing technology;key technology;literature mining;technology opportunity identification

期刊名称： Frontiers in Plant Science

ISSN： 1664-462X

年卷期： 2025 年 16 卷

页码：

收录情况： SCIE（2025版）

摘要： Gene editing technology is a revolutionary biotechnology that has shown great potential and advantages in crop breeding. Current research has proposed many technical methods and design schemes for gene editing technology in crop breeding. However, summarization and analysis are often based on the research and application of a certain technology, lacking a literature content mining perspective to summarize and analyze the application of gene editing and other technologies in crop breeding. At the same time, there is insufficient identification of future research and innovation opportunities of gene editing technology in crop breeding. This study utilized natural language processing, deep learning, and generative topographic mapping (GTM) to conduct an in-depth analysis of the literature on gene editing technology in crop breeding from the perspective of literature mining. Key technical terms in this field were identified, a literature technical map was constructed, technical blank points were identified, and innovative opportunities for blank technology combinations were analyzed. The results showed that from the literature data from 2020 to 2024, 13 technology combinations were identified. These technical contents cover the multi-technology combination strategy of molecular genetic research, the core technology of gene function research in molecular genetics of biotic and abiotic stresses, the technical means of analyzing the molecular mechanisms of stress resistance, the technical scheme of genetic improvement, etc., which provide support for revealing the potential technological innovation opportunities of gene editing technology in the field of crop breeding. This study can scientifically, objectively, and efficiently identify technological innovation opportunities from the literature. Based on the research results, future research should carry out experimental research and application exploration so as to support the application and technological innovation of gene editing technology in crop breeding.

分类号：

  • 相关文献

[1]Genetic gains in grain yield, net photosynthesis and stomatal conductance achieved in Henan Province of China between 1981 and 2008. Xia, X. C.,He, Z. H.,Zheng, T. C.,Yin, G. H.,Wang, L. N.,Han, Y. L.,Huang, F.,Tang, J. W.,Zhang, X. K.,Chen, L.,He, Z. H..

[2]Genetic diversity analysis of abiotic stress response gene TaSnRK2.7-A in common wheat. Zhang, Hongying,Mao, Xinguo,Zhang, Jianan,Chang, Xiaoping,Jing, Ruilian,Zhang, Hongying,Wang, Chengshe.

[3]Genetic Diversity of Common Carpetgrass Revealed by Amplified Fragment Length Polymorphism Markers. Wang, Zan,Kenworthy, Kevin E.,Wang, Zan,Wu, Yanqi,Wang, Zan.

[4]Using Interactome Big Data to Crack Genetic Mysteries and Enhance Future Crop Breeding. Leiming Wu,Linqian Han,Qing Li,Guoying Wang,Hongwei Zhang,Lin Li. 2021

[5]The Research Of Crop Breeding T.echnical Architecture Based On C yber-Physical System(Cps). Yue, E, Zhu, YP. 2017

[6]Breeding Bread-Making Wheat Varieties for Organic Farming Systems: The Need to Target Productivity, Robustness, Resource Use Efficiency and Grain Quality Traits. Rempelos, Leonidas,Wang, Juan,Sufar, Enas Khalid,Almuayrifi, Mohammed Saleh Bady,Knutt, Daryl,Leifert, Halima,Leifert, Alice,Wilkinson, Andrew,Shotton, Peter,Hasanaliyeva, Gultekin,Bilsborrow, Paul,Wilcockson, Steve,Volakakis, Nikolaos,Markellou, Emilia,Zhao, Bingqiang,Jones, Stephen,Iversen, Per Ole,Leifert, Carlo. 2023

[7]The Promising Nanovectors for Gene Delivery in Plant Genome Engineering. Zhi, Heng,Zhou, Shengen,Pan, Wenbo,Shang, Yun,Zeng, Zhanghua,Zhang, Huawei. 2022

[8]A novel method for maize leaf disease classification using the RGB-D post-segmentation image data. Nan, Fei,Song, Yang,Yu, Xun,Nie, Chenwei,Liu, Yadong,Bai, Yali,Zou, Dongxiao,Wang, Chao,Yin, Dameng,Yang, Wude,Jin, Xiuliang. 2023

[9]Editorial: Multi-omics strategies to analyze complex agronomic traits in plants. Lin Chen,Guo Fei Tan. 2023

[10]MtCro: multi-task deep learning framework improves multi-trait genomic prediction of crops. Chao, Dian,Wang, Hao,Wan, Fengqiang,Yan, Shen,Fang, Wei,Yang, Yang. 2025

[11]Genetic Regulation and Breeding Application of Medium-Chain Fatty Acids Metabolism in Rice. Muhammad Zubair,Xiaohong Tong,Aneela Ashraf,Hongzhou Li,Guanghao Li,Ai Xin,Jiale Chen,Yifeng Wang,Zhiyong Li,Jie Huang,Yu Cheng,Jian Zhang,Jiezheng Ying. 2025

[12]A comparative study highlights superiority of LSTM in crop genomic prediction. Ruiqing Pan,Yaolong Yang,Yuanyuan Zhang,Qun Xu,Yue Feng,Junyu Chen,Wei Li,Shoupu He,Xinghua Wei,Mengchen Zhang. 2025

作者其他论文 更多>>