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Updated: Jan 6, 2026

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从横截面的生物数据中学习具有内在噪声的随机过程
Suryanarayana Maddu1, Victor Chardès1, Michael J Shelley1,2
1Center for Computational Biology, Flatiron Institute, New York, NY 10010.
概括
概率流推理 (PFI) 准确地模拟具有内在噪声的生物系统. 这种新方法从奥米克数据中推断出动态模型,优于细胞分化和反应网络的现有方法.
科学领域:
- 计算生物学 计算生物学
- 系统生物学 系统生物学
- 生物物理学的生物物理.
背景情况:
- 从生物数据推断动态模型是具有挑战性的,因为随机过程.
- 欧米克数据通常由在有限的时间点上的独立横截面样本组成.
- 现有的方法往往过于简化或忽略内在系统噪声,影响准确性.
研究的目的:
- 开发一种新的推理方法,准确地模拟随机生物过程.
- 从时间序列的奥米克数据推断底层的扩散过程.
- 从内在的随机性中解脱系统力量.
主要方法:
- 开发了概率流推理 (PFI) 来建模相空间概率流.
- PFI保留了随机过程的依赖时间的边际分布.
- 利用常规微分方程 (ODE) 推断原理来实现算法简化.
主要成果:
- 在PFI下分析证明了Ornstein-Uhlenbeck过程的独特解决方案.
- 在高维度随机反应网络中证明了准确的参数和力估计.
- 通过分子噪声成功推断了细胞分化动态,超越了当前的方法.
结论:
- PFI提供了一个强大的框架,可以从杂的生物数据中推断动态模型.
- 该方法准确地捕捉系统动态,而不会影响优化易度.
- 通过使复杂的生物系统能够更精确地建模,PFI促进了计算生物学的发展.
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