标题： 受扰动下用于基因调控网络发现的可解释神经常微分方程 显示英文标题

标题： Interpretable Neural ODEs for Gene Regulatory Network Discovery under Perturbations

摘要： 现代拥有数千种扰动的高通量生物学数据集为大规模发现表示基因之间调控相互作用的因果图提供了机会。已经提出了可微因果图模型，可以从大规模干预数据集中推断基因调控网络（GRN），捕获由遗传扰动引起的因果基因调控关系。然而，现有模型在表达能力和可扩展性方面受到限制，同时未能解决生物过程（如细胞分化）的动态特性。我们提出了一种名为PerturbODE的新框架，该框架结合了具有生物学信息的神经常微分方程（神经ODE），用于模拟扰动下的细胞状态轨迹，并从神经ODE的参数中推导出因果GRN。我们在模拟和真实过表达数据集上展示了PerturbODE在轨迹预测和GRN推断方面的有效性。 显示英文摘要

摘要： Modern high-throughput biological datasets with thousands of perturbations provide the opportunity for large-scale discovery of causal graphs that represent the regulatory interactions between genes. Differentiable causal graphical models have been proposed to infer a gene regulatory network (GRN) from large scale interventional datasets, capturing the causal gene regulatory relationships from genetic perturbations. However, existing models are limited in their expressivity and scalability while failing to address the dynamic nature of biological processes such as cellular differentiation. We propose PerturbODE, a novel framework that incorporates biologically informative neural ordinary differential equations (neural ODEs) to model cell state trajectories under perturbations and derive the causal GRN from the neural ODE's parameters. We demonstrate PerturbODE's efficacy in trajectory prediction and GRN inference across simulated and real over-expression datasets.