从时间序列数据中推断动态监管交互图,以扰乱的数据.
Dhananjay Bhaskar1,2, Daniel Sumner Magruder2, Matheo Morales1
1Department of Genetics, Yale School of Medicine.
概括
我们介绍了监管时间交互网络推理 (RiTINI),以揭示复杂系统中的动态关系. RiTINI准确地推断出时间变化的交互图,超过了系统生物学和网络科学的传统方法.
科学领域:
- 复杂系统科学 复杂系统科学
- 网络科学 网络科学
- 计算生物学 计算生物学
背景情况:
- 复杂的系统表现出动态相互作用,这对于行为预测至关重要.
- 准确推断这些动态关系仍然是传统方法的挑战.
研究的目的:
- 开发一种用于推断复杂系统中时间变化的交互图的新方法.
- 克服传统因果推理网络的局限性,这些网络可以推断静态和非循环图.
主要方法:
- 调节时间交互网络推理 (RiTINI) 结合了空间和时间图的注意力与图神经常规微分方程 (ODEs).
- RiTINI利用时间间隔信号和节点扰动来捕获系统动态.
- 采用图形注意力以适应性聚焦相关交互和图形神经ODE用于连续时间建模.
主要成果:
- RiTINI成功地推断了循环,定向和时间变化的图表,提供了更全面的表示.
- 在动态系统,神经网络和基因调控网络的模拟上推断交互图的最新性能.
结论:
- RiTINI提供了一种强大而准确的方法,用于模拟复杂系统中的动态相互作用.
- 该方法推进了网络推理领域,特别是对于生物和动态系统.
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