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相关概念视频

Causality in Epidemiology01:21

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Causality or causation is a fundamental concept in epidemiology, vital for understanding the relationships between various factors and health outcomes. Despite its importance, there's no single, universally accepted definition of causality within the discipline. Drawing from a systematic review, causality in epidemiology encompasses several definitions, including production, necessary and sufficient, sufficient-component, counterfactual, and probabilistic models. Each has its strengths and...
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 Building a Survival Tree
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Sequence Networks of Rotating Machines01:24

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A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
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相关实验视频

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Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
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以树为基础的添加噪音为导向的非循环图形模型,用于与相互作用的非线性因果发现.

Fangting Zhou1, Kejun He2, Yang Ni3,4,5

  • 1Department of Biostatistics, Yale University, New Haven, CT 06520, United States.

Biometrics
|July 24, 2025
PubMed
概括

我们引入了基于树的附加噪声模型,用于可解释的非线性因果发现,有效地处理复杂的相互作用. 这种方法增强了系统生物学和社会科学等领域的因果推理.

关键词:
添加式噪声模型的模型.贝叶斯网络是一个贝叶斯网络.可识别因果关系的原因.因果相互作用因果相互作用结构方程模型的结构方程模型.树木 树木 树木 树木

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科学领域:

  • 因果推理因果推理
  • 机器学习 机器学习
  • 系统生物学 系统生物学

背景情况:

  • 有添加噪声的定向非循环图形模型对于非线性因果发现至关重要.
  • 现有的模型往往限制因果函数是附加的,限制它们在存在因果相互作用时的适用性.
  • 目前用于一般非线性因果函数的方法可能是计算密集型或缺乏可解释性.

研究的目的:

  • 为非线性因果发现提出一种新,可解释和计算可行的方法,该方法包含了相互作用.
  • 开发基于树的增材噪声模型 (TANM),有效地处理复杂的因果关系.
  • 为TANMs建立新的识别条件和算法.

主要方法:

  • 开发基于树的添加噪声模型 (TANM),以表示与相互作用的非线性因果函数.
  • 导出新的因果识别条件,这些条件特定于由树结构产生的断片常数因果函数.
  • 实现源节点识别的递归算法和基于分数的排序搜索算法.

主要成果:

  • 通过广泛的模拟来证明TANM的实用性和计算可行性.
  • 与现有的添加噪声模型相比,TANM的表现优越,特别是强烈的因果相互作用.
  • 成功应用了该方法来推断与乳腺癌相关的蛋白质-蛋白质相互作用网络.

结论:

  • 基于树的增材噪声模型为与相互作用的非线性因果发现提供了强大而可解释的解决方案.
  • 提出的可识别性条件和算法可以在复杂系统中进行可靠的因果推理.
  • 这种方法对系统生物学和其他需要因果网络推断的领域的应用具有重大潜力.