在Brassica napus中解读热应激耐受性的核心分子机制,使用转录学和机器学习
Muhammad Ikram1,2, Muhammad Farhan3, Behnam Derakhshani4
1School of Breeding and Multiplication (Sanya Institute of Breeding and Multiplication), Yazhou District, Hainan University, Huanjin Road, Sanya, 572025, China.
Plant cell reports
|March 17, 2026
概括
机器学习与生物信息学相结合,确定了Brassica napus中热应激耐受性的关键基因. 这项研究增强了对这种重要的油作物的耐热性理解.
科学领域:
- 基因组学和植物科学 植物科学
- 生物信息学和计算生物学
- 机器学习在农业中的应用
背景情况:
- 热应激严重威胁到Brassica napus的种植,影响了全球油种子的生产.
- 传统方法在大型转录数据集中难以识别耐热基因.
- 了解热应激机制对于作物改善至关重要.
研究的目的:
- 通过综合生物信息学和机器学习,阐明Brassica napus的耐热机制.
- 确定候选基因和影响热应激耐受性的关键调节特征.
- 用实验方法验证发现.
主要方法:
- 结合了传统的生物信息学 (DEG分析,WGCNA) 和机器学习 (随机森林,SHAP分析).
- 分析了来自种子,花朵,叶子和质组织的转录组数据.
- 使用RT-qPCR验证了关键基因表达.
主要成果:
- 确定了1179个与应激反应和新陈代谢相关的差异表达基因 (DEGs).
- 在WGCNA的调查中,发现了45个候选枢纽基因.
- 机器学习模型,特别是随机森林,实现了高精度 (0.98 ROC,0.89精度).
- 影响热耐受性的21个主要特征 (基因) 被排名,其中8个被ML独特识别.
- 八个关键监管者的表达得到了验证.
结论:
- 机器学习和可解释的AI有效地识别了热应激耐受性的关键调节者.
- 这项研究为改善 Brassica napus. 的耐热性提供了宝贵的资源.
- 突出了将omics数据与先进的计算方法集成的力量.
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