机器学习和功能验证将OsRAV11/12确定为干旱耐受性和大米早期开花的负调节者
You Zhang1, Muhammad Ikram1, Naveed Khan2
1Hainan Yazhou-Bay Seed Laboratory, School of Breeding and Multiplication, Hainan University, Sanya, 572025, China; Collaborative Innovation Center of Nanfan and High-Efficiency Tropical Agriculture, School of Tropical Crops and Forestry, Hainan University, Haikou, 570228, China.
International journal of biological macromolecules
|September 19, 2025
概括
机器学习确定了OsRAV11/12作为大米干旱耐受性的负调节剂. 淘汰线显示出增强的弹性,而过度表达线则易受影响,揭示了改善作物的关键遗传机制.
科学领域:
- 植物生物学 植物生物学
- 遗传学 遗传学 是一个
- 生物技术是生物技术.
背景情况:
- 米 (Oryza sativa L.) 是一个关键的全球粮食来源,受到干旱压力的威胁.
- 识别干旱耐受性的遗传调节者对于作物改善至关重要.
研究的目的:
- 利用机器学习 (ML) 识别调节大米干旱应激反应的关键基因.
- 为了功能验证候选基因,特别是OsRAV11/12,作为干旱耐受性的调节者.
主要方法:
- 训练随机森林,XGBoost和Feedforward神经网络模型在米的转录基因数据上.
- 在基因淘汰 (KO) 和过度表达 (OX) 实验中使用CRISPR/Cas9.
- 进行了生理和转录组分析,以评估干旱反应.
主要成果:
- XGBoost模型在预测干旱耐受性基因方面取得了90%的准确性.
- OsRAV11/12被确定为负调节剂;KO线显示80-85%的存活率与野生类型的57.5%相比.
- KO 线表现出改善的生理特征 (更低的口腔导电率,更高的 ABA) 和应激反应基因表达.
结论:
- OsRAV11/12对大米的干旱耐受性进行负面调节.
- 机器学习是一个强大的工具,可以加速发现控制复杂特征的基因,例如抗旱能力.
- 研究结果为开发适应气候变化的米品种提供了目标.
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