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Background and Environment Affect Phenotype02:27

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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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Gene expression is a dynamic process that is significantly influenced by environmental factors. This interaction underlies the complex nature of biological development and the phenotypic differences observed among individuals, even among those with identical genetic makeups. Factors such as radiation, temperature, behavior, nutrition, and stress play pivotal roles in determining how genes are expressed. The concept of the reaction range is central to understanding this interaction. It posits...
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Behavior genetics explores how genetic inheritance influences human behavior. It focuses on how genes, passed from parents to offspring, contribute to the development of behavioral traits and tendencies. This branch of genetics seeks to understand the complex interplay between inherited genetic factors and environmental influences in shaping our behaviors.
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相关实验视频

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一种基于混合深度学习的方法,通过环境选择来获得最佳的基因型.

Zahra Khalilzadeh1, Motahareh Kashanian1, Saeed Khaki1

  • 1Department of Industrial and Manufacturing Systems Engineering, Iowa State University, Ames, IA, United States.

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概括

机器学习模型通过分析基因型和天气数据准确预测大豆产量. 这种数据驱动的方法有助于开发适应气候变化的作物,并为特定环境选择最佳的基因型.

关键词:
一般化合奏方法一般化合奏方法卷积神经网络是一种卷积神经网络.农作物产量预测预测特性重要性分析 特性重要性分析基因型选择 基因型选择基因型与环境的相互作用.

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科学领域:

  • 农业科学 农业科学
  • 计算生物学 计算生物学
  • 气候科学 气候科学

背景情况:

  • 在不同的天气条件下准确预测作物产量对于开发耐气候作物品种至关重要.
  • 基因型与环境的相互作用显著影响作物反应,但很难将其纳入育种计划.
  • 机器学习提供了一种数据驱动的解决方案,通过计算基因型与环境相互作用来预测产量.

研究的目的:

  • 开发和评估用于预测大豆产量的机器学习模型.
  • 调查集合方法在提高产量预测准确度方面的有效性.
  • 确定影响大豆产量预测的关键因素.

主要方法:

  • 开发了两个卷积神经网络 (CNN) 模型:CNN模型和CNN-LSTM模型,使用大豆杂交产量的大量数据集.
  • 应用了通用集合方法 (GEM) 来结合和优化基于CNN的模型.
  • 对未见的基因型-位置组合和分析的特征重要性进行模型性能评估.

主要成果:

  • 与单个CNN-LSTM和CNN模型相比,GEM整体方法显著提高了预测准确性 (RMSE和MAE).
  • 位置,基因型和年份被确定为大豆产量的最关键预测因素.
  • 整合州级土壤数据并没有显著提高模型的预测能力.

结论:

  • 拟议的数据驱动的GEM模型为基因型选择提供了有价值的工具,特别是在测试年限有限的场景中.
  • 机器学习,特别是组合方法,可以有效地模拟复杂的基因型-环境相互作用,用于产量预测.
  • 关键的天气变量,如最大直接正常辐射和平均降水,对于准确的产量预测至关重要.