从非顺序数据 (DyNoSeD) 中识别随机动态
Zhixin Lu1, Łukasz Kuśmierz1, Stefan Mihalas1,2
1Allen Institute, 615 Westlake Ave. N, Seattle, Washington 98109, USA.
Chaos (Woodbury, N.Y.)
|February 2, 2026
概括
我们开发了DyNoSeD,这是一个新的框架,可以从非顺序数据中推断随机动态. 这种方法克服了标准时间序列分析对于无序,受限区域测量的局限性.
科学领域:
- * 计算生物学 * 计算生物学
- * 系统生物学 系统生物学
- * 数据科学数据科学
背景情况:
- *推断随机动态对于理解复杂系统至关重要.
- * 标准的时间序列方法在无序,非顺序的数据中失败,这在现实应用中很常见.
- *有限的状态空间采样进一步复杂化了动态系统的识别.
研究的目的:
- * 介绍DyNoSeD (从非顺序数据中识别动态),这是一个第一原则框架.
- *通过最小化福克-普朗克余值,使非顺序数据的动态参数推断成为可能.
- * 提供可靠的系统识别方法,即使数据有限或无序.
主要方法:
- * 开发了两条互补的路线:一个局部路线用于区域限制数据,一个使用内核Stein差异的全球路线.
- * 用福克-普朗克方程余数进行参数推理.
- * 应用了基于梯度的优化,用于一般的非亲系参数化.
主要成果:
- * 建立了参数独特性条件,并为亲缘动态学推导了灵敏度分析.
- *成功地恢复了使用本地和全球路线的随机罗伦茨系统的参数.
- * 通过使用全球路线,从未排序的稳定状态样本中确定了基因调节网络相互作用矩阵.
结论:
- * DyNoSeD提供了两种新的第一原则路线,用于从非顺序数据中识别系统.
- * 框架有效地将数据,密度和随机动态联系在一起.
- * DyNoSeD提供了一个强大的工具,用于分析具有有限或非顺序测量的复杂系统.
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