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相关概念视频

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Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
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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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提高大豆种群稀疏测试设计中的预测能力.

Reyna Persa1, Caio Canella Vieira2, Esteban Rios1

  • 1Agronomy Department, University of Florida, Gainesville, FL, United States.

Frontiers in genetics
|December 11, 2023
PubMed
概括
此摘要是机器生成的。

基于基因组的预测模型提高了大豆育种效率. 最大限度地提高遗传多样性,并在训练组中使用重叠的线条,以提高优质基因型选择的预测准确性.

关键词:
实验设计 实验设计基因组预测 基因组预测基因型与环境的相互作用.植物育种 植物育种豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆测试很少,测试很少.

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科学领域:

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

背景情况:

  • 高维基因组数据和基于基因组的预测 (GP) 模型加速了大豆育种中的遗传收益.
  • 基于GP的稀疏测试优化了基因型评估能力并降低了成本.
  • 这项研究开创了基于GP的大豆稀疏测试实施.

研究的目的:

  • 评估训练组的组成和大小对大豆GP预测能力的影响.
  • 为了研究重叠 (O-RILs) 和非重叠 (NO-RILs) 重组杂交系 (RILs) 在训练集中的有效性.
  • 评估在固定大小的培训集中最大限度地提高遗传多样性的方法.

主要方法:

  • 在9个环境中测试的39个大豆巢协会映射 (NAM) 种群中利用了1755个RIL.
  • 评估预测能力使用各种GP模型与不同的训练集大小和组成 (O-RILs与NO-RILs).
  • 在固定尺寸的训练样本中使用方法最大化或最小化遗传多样性.

主要成果:

  • 减少训练集大小通常会降低大多数组合中的预测能力.
  • 最大限度地提高遗传多样性,并包括O-RIL,提高了固定训练集大小的预测准确性.
  • 最复杂的GP模型对训练组组成和多样性因素的敏感性较小.

结论:

  • 基于GP的稀疏测试对于大豆育种是有效的,培训组的组成显著影响了准确性.
  • 纳入遗传多样性和重叠的RIL是提高预测性能的关键策略.
  • 在繁殖管道早期增加环境测试有助于选择稳定,广泛适应的基因型.