标题： 可解释的神经ODE用于扰动下的基因调控网络发现 显示英文标题

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

摘要： 现代具有数千种扰动的高通量生物数据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。 许多方法已被提出以推断对应于捕获因果基因关系的潜在基因调控网络（GRN）的有向无环图（DAG）。 然而，现有模型存在限制性假设（例如，线性、无环性）、可扩展性有限和/或无法解决生物学过程（如细胞分化）的动态特性。 我们提出了 PerturbODE，一种新颖的框架，该框架结合了具有生物学信息的神经微分方程（神经ODE）来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. Numerous methods have been proposed to infer a directed acyclic graph (DAG) corresponding to the underlying gene regulatory network (GRN) that captures causal gene relationships. However, existing models have restrictive assumptions (e.g. linearity, acyclicity), limited scalability, and/or fail 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.