用可解释的神经微分方程建模基因扰动下的动态变化，推断调控网络。
Interpretable Neural ODEs for Gene Regulatory Network Discovery under Perturbations
- 通过可解释的神经微分方程建模细胞状态轨迹变化
- 从模型参数中提取因果调控网络，支持未见扰动的模拟预测
- 将基因聚类为可解释的共调控模块，适用于真实与仿真数据
包含数千次扰动的高通量生物数据使大规模发现基因间调控关系的因果图成为可能。现有可微因果图模型和基于回归的方法虽能从干预数据中推断基因调控网络（GRNs），但难以捕捉细胞分化等非线性生物过程动态。为此，我们提出PerturbODE框架，利用可解释的神经常微分方程（neural ODEs）建模扰动下的细胞状态轨迹，并从神经ODE参数中推导出底层因果GRN，支持对未见遗传干预的下游模拟。该GRN通过单隐层前馈网络编码，隐式地将基因分组为可解释的共调控模块。我们在模拟与真实过表达数据集上验证了PerturbODE在GRN推断及扰动响应预测方面的有效性。
原文摘要 · Abstract (English)
Modern high-throughput biological datasets containing thousands of perturbations enable large-scale discovery of causal graphs that represent regulatory interactions between genes. Differentiable causal graphical models and regression-based methods have been developed to infer gene regulatory networks (GRNs) from interventional datasets. However, existing approaches fail to capture the non-linear dynamics of biological processes such as cellular differentiation. To address this limitation, we propose PerturbODE, a novel framework that employs interpretable neural ordinary differential equations (neural ODEs) to model cell state trajectories under perturbations and derive the underlying causal GRN from the neural ODE parameters, enabling downstream simulation of unseen genetic interventions. The GRN is encoded via a single-hidden-layer feedforward network, implicitly grouping genes into interpretable co-regulated modules. We demonstrate PerturbODE's efficacy in GRN inference and extension to perturbation response prediction across both simulated and real overexpression datasets.
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