使用逻辑回归的随机反应网络的推断结构和参数
Boseung Choi1,2,3, Hye-Won Kang4, Grzegorz A Rempala3
1Korea University Sejong Campus, Sejong, South Korea.
PloS one
|February 12, 2026
概括
本研究介绍了物流回归方法,以从时间序列数据中识别化学反应网络结构和参数. 这些工具为合成和现实世界的流行病模型提供了机械洞察力,包括COVID-19的动态.
科学领域:
- 系统生物学 系统生物学
- 计算生物学 计算生物学
- 流行病学 流行病学
背景情况:
- 化学反应网络的建模是复杂的,因为在识别网络结构和估计反应参数方面存在挑战.
- 随机反应系统需要强大的方法来从观察到的数据中推断网络属性.
研究的目的:
- 开发基于概率的方法,使用多项逻辑回归来推断在随机反应系统中的固态度和网络连接.
- 展示这些方法在恢复各种模型的网络结构和分析现实世界流行病数据中的应用.
主要方法:
- 在随机反应系统的全时间序列轨迹上利用了多项逻辑回归.
- 应用贝叶斯逻辑回归与部分可观测系统的微分方程建模相结合,以SIR模型为例.
主要成果:
- 在Togashi-Kaneko,热冲击蛋白质网络和SIR模型等模型中成功恢复了固态度系数和网络结构.
- 从合成COVID-19类流行病数据中证明了核心SIR参数的可靠恢复,即使部分可观测性.
结论:
- 基于概率的工具,如物流回归,为化学反应网络提供了有意义的机制性见解.
- 开发的框架对于分析合成和现实世界流行病动态,包括COVID-19爆发等复杂情景的有效.
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