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Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
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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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Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
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Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
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Regression analysis is a statistical tool that describes a mathematical relationship between a dependent variable and one or more independent variables.
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A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
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Updated: Sep 15, 2025

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优化基因组预测与转移学习在脊柱回归框架下的基因组预测.

Osval A Montesinos-López1, Eduardo A Barajas-Ramirez1, Josafhat Salinas-Ruiz2

  • 1Facultad de Telemática, Universidad de Colima, Colima, México.

The plant genome
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PubMed
概括
此摘要是机器生成的。

基因组选择 (GS) 的准确性通过转移学习得到改善. 转移RR和转移ARR方法在小麦和大米育种中提高了相关性预测的22%以上,NRMSE的5%以上.

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科学领域:

  • 农业科学 农业科学
  • 遗传学 遗传学 是一个
  • 生物技术是生物技术.

背景情况:

  • 基因组选择 (GS) 对于在没有直接表型化的情况下预测植物和动物特征至关重要.
  • 由于实验噪声,提高GS的预测准确度至关重要.
  • 为了提高GS预测准确度,存在各种策略.

研究的目的:

  • 探索转移学习在基因组选择中的应用.
  • 评估转移学习与回归 (RR) 和分析RR (ARR) 结合,以提高预测.
  • 将转移学习模型与传统的RR和ARR进行比较.

主要方法:

  • 应用转移学习 (转移RR和转移ARR) 从代理到目标环境.
  • 利用了11个小麦和大米的多环境数据集.
  • 使用皮尔森相关性 (Cor) 和正常化根平均平方误差 (NRMSE) 评估模型性能.

主要成果:

  • 转移RR和转移ARR显著改善了预测性能.
  • 与非转移模型相比,转移学习方法提高了Cor的22.962%和NRMSE的5.757%.
  • 经验证据支持GS转移学习的有效性.

结论:

  • 转移学习,特别是转移RR和转移ARR,提供了一种强大的方法来提高基因组选择的准确性.
  • 这些方法显示出改善育种计划的巨大潜力.
  • 这些发现突显了利用跨环境的数据进行更准确的预测的价值.