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相关概念视频

Background and Environment Affect Phenotype02:27

Background and Environment Affect Phenotype

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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.
An example of how genetic background affects phenotype can be seen in horses. The Extension gene in horses is responsible for their coat color. A wild-type gene (EE) produces black pigment in the coat, while a mutant gene (ee) produces red pigment. A...
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相关实验视频

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使用生物解释的神经网络在多队列多omics数据上的表型预测.

Arno van Hilten1, Jeroen van Rooij2,

  • 1Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands. a.vanhilten@erasmusmc.nl.

NPJ systems biology and applications
|August 2, 2024
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概括

我们使用多omics数据开发可解释的神经网络,用于精准医学. 这些"可见网络"准确地预测吸烟状况和年龄等特征,提供生物学见解.

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科学领域:

  • 基因组学和生物信息学
  • 计算生物学 计算生物学
  • 系统生物学 系统生物学

背景情况:

  • 整合多学科数据对于推进精准医学至关重要.
  • 需要可解释的预测模型来理解复杂的生物机制.
  • 当前的方法往往缺乏透明度在多omics数据分析.

研究的目的:

  • 开发可解释的神经网络,称为"可见网络",用于多omics数据分析.
  • 评估可见网络的性能,可解释性和可通用性,以预测特征.
  • 为了比较多个omics可见网络与单个omics网络.

主要方法:

  • 利用BIOS联盟 (N=2940) 的全基因组RNA表达和CpG甲基化数据.
  • 通过先前的生物知识来建立可见网络,利用神经网络.
  • 进行了群组智能交叉验证,以评估诊断性能和解释一致性.

主要成果:

  • 可见网络在预测吸烟状态 (AUC=0.95) 和推断年龄 (误差=5.16年) 中取得了高准确性.
  • 多omics可见网络表现出比单omics网络更好的性能,稳定性和通用性.
  • 确定了关键基因 (例如,AHRR,GPR15,COL11A2) 在队列中始终具有可预测性.

结论:

  • 可见神经网络提供了一个强大的,可解释的方法来整合多omics数据.
  • 这些模型为潜在的特征和疾病的生物学机制提供了新的见解.
  • 可见网络显示出通过强大的多omics分析来增强精准医学的巨大潜力.