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使用机器学习算法,对与玉米生物和非生物压力显著相关的基因进行预测优先排序

Anjan Kumar Pradhan¹, Prasad Gandham¹, Kanniah Rajasekaran²

¹School of Plant, Environmental and Soil Sciences, Louisiana State University Agricultural Center, Baton Rouge, LA, United States.

Frontiers in plant science

|July 4, 2025

概括

研究人员使用机器学习来识别玉米中的关键基因,帮助它抵抗生物和非生物压力. 这项研究优先考虑了235个候选基因,推动了开发更有弹性的作物品种的努力.

科学领域:

植物科学植物科学
基因组学就是基因组学.
生物技术是生物技术.

背景情况:

像玉米这样的作物面临着来自生物和非生物压力的重大威胁,影响全球粮食安全.
确定抗压力遗传因素对于提高玉米的弹性和生产率至关重要.

研究的目的:

使用元转录组方法识别和优先考虑参与玉米抗压力的关键基因和监管网络.
通过机器学习分析来预测生物,非生物和联合压力条件的顶级候选基因.

主要方法:

分析了39756个在压力下在玉米中差异表达的基因的元转录组数据集.
七个机器学习模型 (SVM,PLSDA,KNN,GBM,RF,NB,DT) 用于基因优先级.
使用特征选择和加权基因共同表达网络分析来识别枢纽基因.

主要成果:

在所有模型和压力条件中确定了235个独特的候选基因.
三个基因 (编码bZIP TF 68,富含甘氨酸的蛋白质2,ALDH11) 是无生物和组合应激的最佳候选者.
一个基因 (编码RNA结合蛋白AU-1/Ribonuclease E/G) 在生物和非生物压力下普遍表达.

结论:

机器学习有效地优先考虑了玉米抗压力的候选基因.
识别的枢纽基因和常常表达的基因提供了功能验证的目标.
需要进一步研究功能验证,以开发抗压性玉米品种.

关键词:

在RNA-Seqq.一些生物) 压力.人工智能的人工智能是人工智能.基因表达的基因表达方式玉米玉米玉米是一种

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