生物网络重建潜伏混因子下的动态因果关系
IEEE transactions on pattern analysis and machine intelligence
|January 28, 2026
概括
这项研究引入了一种新的方法来推断生物系统中的因果相互作用,即使有未观察到的混因素. 该方法有效地重建生物网络,并从时间序列数据中识别隐藏的混因素.
科学领域:
- 系统生物学 系统生物学
- 计算生物学 计算生物学
- 网络科学 网络科学
背景情况:
- 因果相互作用推断受到由于生物系统混而导致的虚假相互作用的挑战.
- 现有的方法在潜在的 (未观察到的) 混因素下推断因果相互作用时扎.
研究的目的:
- 开发一种用于推断潜伏混因素下的动态因果关系的方法.
- 从时间序列数据中重建潜在的混因素.
- 为解决长期存在的因果检测在具有有限观测变量的高维系统中的长期问题.
主要方法:
- 提出了一种基于在延迟嵌入空间中的直角分解定理的新方法.
- 在高维系统中开发了因果检测的理论基础.
- 启用了嵌入空间中合变量的分离,以解决不可分离的问题.
主要成果:
- 在潜在的混因素下成功推断出动态因果关系.
- 从时间序列数据有效重建隐藏的混因子.
- 证明了该方法的因果检测能力,即使在高维系统中仅有两个观察到的变量.
结论:
- 拟议的方法,CIC,克服了虚假因果相互作用和潜在混因素的挑战.
- 正交分解定理为因果推理提供了坚实的理论基础.
- 在真实数据集上验证的有效性,用于重建生物网络和未观察到的混因素.
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