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相关概念视频

Multiple Regression01:25

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Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
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Although the genetic makeup of an organism plays a major role in determining the phenotype, there are also several environmental factors, such as temperature, oxygen availability, presence of mutagens, that can alter an organism’s phenotype.
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The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
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In order to produce glucose, plants need to capture sufficient light energy. Many modern plants have evolved leaves specialized for light acquisition. Leaves can be only millimeters in width or tens of meters wide, depending on the environment. Due to competition for sunlight, evolution has driven the evolution of increasingly larger leaves and taller plants, to avoid shading by their neighbors with contaminant elaboration of root architecture and mechanisms to transport water and nutrients.
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Imaging and Analysis for Quantifying Maize (Zea mays) Abiotic Stress Phenotypes
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在玉米多环境试验中使用统计和机器学习方法进行基因组预测.

Cynthia Aparecida Valiati Barreto1, Kaio Olimpio das Graças Dias2, Ithalo Coelho de Sousa3

  • 1Department of Statistics, Universidade Federal de Viçosa, Viçosa, Minas Gerais, Brazil.

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基因组预测在多环境试验 (MET) 中准确预测玉米杂交的性能. 机器学习和基因组最佳线性无偏预测 (GBLUP) 都显示出效率,最佳方法取决于特定的育种计划需求.

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科学领域:

  • 农业科学 农业科学
  • 植物育种 植物育种
  • 遗传学 是一个遗传学.

背景情况:

  • 基因组预测为传统的现场试验提供了经济有效的替代方案,用于在多环境试验 (MET) 中评估未经测试的单交叉杂交.
  • 准确预测混合动力性能对于加速玉米育种计划至关重要.

研究的目的:

  • 评估基因组预测谷物产量和雌性开花时间在MET内未经测试的单交叉玉米杂交品种中.
  • 探索和比较机器学习方法与基因组最佳线性无偏预测 (GBLUP) 与MET中混合预测的非添加效应.

主要方法:

  • 基因组预测模型被应用于预测单个交叉杂交的表型,这些杂交并未包括在实地试验中.
  • 研究了机器学习方法,并与GBLUP进行了比较,考虑了非添加性遗传效应.

主要成果:

  • 机器学习和GBLUP都在不同环境中预测混合性能方面表现出了效率.
  • 预测完全新的杂交物比预测稀疏测试设计中的杂交物更具挑战性.
  • 最优的预测方法取决于上下文,需要对GBLUP进行仔细的方差组件建模,或者利用机器学习的能力在没有先前假设的情况下捕获非附加效应.

结论:

  • 基因组预测,利用GBLUP和机器学习,是玉米育种计划的宝贵工具.
  • 精确的差异组件建模是优化GBLUP的关键,而机器学习在捕捉复杂的遗传相互作用方面提供了灵活性.