研究人员引入了概率流匹配（PFM），一个旨在从时间分辨的单细胞测量中学习具有生物物理一致性的随机过程的新框架。该方法旨在提高基因调控动力学推断的机制可解释性和泛化能力，而这一直是当前方法的局限性。PFM应用于造血作用数据集，证明了其准确捕捉谱系转换和基因扰动反应的能力，并推断了细胞增殖和死亡动力学。 AI
影响 为将机制建模与单细胞组学数据相结合提供了一个新框架。
排序理由 介绍生物学建模新方法的学术论文。
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arXiv:2604.25062v1 Announce Type: cross Abstract: Cellular differentiation is governed by gene regulatory networks, the high-dimensional stochastic biochemical systems that determine the transcriptional landscape and mediate cellular responses to signals and perturbations. Althou…
Cellular differentiation is governed by gene regulatory networks, the high-dimensional stochastic biochemical systems that determine the transcriptional landscape and mediate cellular responses to signals and perturbations. Although single-cell RNA sequencing provides quantitativ…