从模型到作物保存的NUE规则增强了机器学习对使用效率的预测
Ji Huang1, Chia-Yi Cheng1,2, Matthew D Brooks3
1Department of Biology, Center for Genomics and Systems Biology, New York University, NY, USA.
The Plant cell
|May 14, 2025
概括
我们开发了一条管道,用于识别转录因子 (TF) 规律,用于作物中使用效率 (NUE). 这种方法结合了基因调节网络推断和机器学习,以改善作物特征的发展.
科学领域:
- 植物生物学 植物生物学
- 基因组学就是基因组学.
- 系统生物学 系统生物学
背景情况:
- 鉴定农业特征的基因调节网络 (GRNs) 是至关重要的,但在作物中具有挑战性.
- 验证这些网络对于理解和改善作物表现至关重要.
研究的目的:
- 开发和验证转录因子 (TF) 规则,管理作物中使用效率 (NUE).
- 通过综合系统生物学和机器学习建立作物特征改进的战略框架.
主要方法:
- 进行了时间过程转录组分析,以确定玉米和阿拉比多普西斯的保存 (N) 反应级联.
- 使用基于细胞的TF扰动试验 (TARGET) 推断N调节的GRNs和修剪的高置信TF目标边缘.
- 利用XGBoost的机器学习来学习基因对NUE的特征得分,并将它们集成到N-GRN中,根据NUE Regulon得分对TF进行排名.
主要成果:
- 在玉米和Arabidopsis中确定了保存的时间N响应级联和推断的因果TF目标边缘.
- 使用TARGET测试验证实了顶级TF的NUE规则,证明了它们在NUE中的作用.
- 经过验证的NUE规则中的基因显著提高了机器学习模型中NUE特征的预测准确度.
结论:
- 开发的管道有效地识别和验证TF规则用于作物特征改进.
- 集成GRN推断,机器学习和正统网络分析,为增强NUE提供了一个强大的框架.
- 这种方法为加速改进作物品种的开发提供了战略途径.
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