大数据和人工智能辅助的作物育种:进展和前景
Wanchao Zhu1,2, Weifu Li3,4, Hongwei Zhang5
1Key Laboratory of Biology and Genetic Improvement of Maize in Arid Area of Northwest Region, College of Agronomy, Northwest A&F University, Yangling, 712100, China.
Journal of integrative plant biology
|October 28, 2024
概括
生物大数据和人工智能的进步加速了作物育种. 智能精密设计育种 (IPDB) 为未来的作物开发提供了可预测,高效和具有成本效益的方法.
科学领域:
- 农业科学 农业科学
- 遗传学 遗传学 是一个
- 生物信息学是一种生物信息学.
背景情况:
- 基因发现,生物大数据 (BBD) 和人工智能 (AI) 的快速进展正在改变农业.
- 全球日益增长的粮食需求需要加快作物育种策略.
研究的目的:
- 审查当前的作物育种方法,并确定创新需求.
- 探索BBD和AI的集成,以进行先进的遗传分析和预测.
- 提出智能精密设计育种 (IPDB) 作为一种新的AI驱动的育种范式.
主要方法:
- 审查现有的育种技术和关于BBD和AI应用的文献.
- 对人工智能和BBD集成进行基因剖析,功能基因探索和表型预测的分析.
- 概念化和提出IPDB框架及其实施策略.
主要成果:
- BBD和AI的整合使得增强的遗传剖析,功能性基因发现和准确的表型预测成为可能.
- 拟议的IPDB框架旨在提高作物育种的可预测性,效率和成本效益.
- 通过将生物技术,生物信息学和育种专业知识整合到一个合作系统中,CropGPT是IPDB的典范.
结论:
- 由人工智能驱动的IPDB是当前作物育种技术的重大进步.
- IPDB为各种利益相关者提供综合平台和服务,促进在作物改进方面的合作.
- 拟议的系统非常适合解决未来在作物育种和粮食安全方面的挑战.
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