为了在作物改进中实现人工智能预测的总体框架
Carlos Messina1,2, Julian Garcia-Abadillo3, Owen Powell4,5
1UF/IFAS Crop Transformation Center, University of Florida, Gainesville, FL, USA. cmessina@ufl.edu.
概括
人工智能 (AI) 和组合预测为作物改进提供了一个新的框架. 这种方法通过整合生物知识和计算方法来提高预测准确性,加速复杂特征的遗传收益.
科学领域:
- 农业科学 农业科学
- 计算生物学 计算生物学
- 遗传学 是一个遗传学.
背景情况:
- 复杂特征的基因组预测面临由于维度的诅咒而面临的挑战.
- 现有的方法很难有效地利用复杂的遗传和生理网络信息.
研究的目的:
- 介绍人工智能 (AI) 支持的作物改进预测理论框架.
- 使用物流地图模型展示框架的功能和局限性.
主要方法:
- 集成动态系统建模,集合方法,贝叶斯统计学和优化.
- 利用后勤地图来模拟系统复杂性和评估可预测性.
- 系统状态与系统过程速率的比较预测.
主要成果:
- 使用象征性/亚象征性AI时,预测技能随着系统复杂性的增加而增加.
- 继承性和可预测性随着系统复杂性的增加而下降,与经验数据保持一致.
- 预测系统过程速率比预测复杂系统的系统状态更有效.
结论:
- 支持人工智能的预测框架可以克服基因组预测中的维度诅咒.
- 利用先前的生物学知识和计算方法可以提高预测的准确性.
- 这种综合方法有望加速作物育种计划中的遗传收益.
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