增强因果全向网络的重建 (RECON)
Reconstruction of Enhanced Causal Omnidirectional Network (RECON)
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中文总结 AI 辅助
针对从离散观测状态轨迹重建调控网络的难题，提出RECON方法，利用基于积分的加性非参数ODE模型，通过五个方法改进，在模拟研究中表现出色，能有效减少虚假边，准确重建调控网络。
中文摘要 AI 辅助
从p个离散观测的状态轨迹中学习动态系统并重建潜在调控网络仍然是具有挑战性的问题。现有方法往往产生大量虚假边并存在方法局限性。我们提出了一种新方法RECON，利用基于积分的加性非参数ODE模型从p个时间序列数据重建调控网络。RECON包含五个方法改进。它结合新的数据驱动边选择程序，大幅减少虚假边，重建全向网络捕捉因果调控关系，适应不同采样场景，将节点轨迹和边调控效应建模为随时间变化的函数，重建带符号和加权的调控网络并提供全面解释。在五个模拟研究中，RECON始终优于GRADE，几乎消除所有虚假边，保留几乎所有真实调控边。在最具挑战性的场景中，虚假边数量从239减少到0。
英文摘要
Learning a dynamical system and reconstructing the underlying regulatory network from $p$ discretely observed state trajectories remain challenging problems. Existing approaches often produced a large number of spurious edges and suffered from several methodological limitations. We propose a new approach, Reconstruction of Enhanced Causal Omnidirectional Network (RECON), that leverages an integral-based additive nonparametric ODE model to reconstruct regulatory networks from $p$ time-course data. RECON incorporates five methodological advances. First, it incorporates a new data-driven edge selection procedure that substantially reduces spurious edges while preserving true regulatory edges. Second, it reconstructs an omnidirectional network that captures causal regulatory relationships rather than merely statistical associations or noise artifacts. Third, it substantially broadens the applicability of standard ODE-based approaches by accommodating both dense regular and sparse irregular longitudinal sampling scenarios. Fourth, it models both node trajectories and edge regulatory effects as time-varying functions, emphasizing a dynamic regulatory network. Fifth, it reconstructs a signed and weighted regulatory network and provides comprehensive network interpretation through two-way direction, activatory/inhibitory indicator, and strength, together with keystone node identification and topological structure. Across five simulation studies, RECON consistently outperforms GRADE by removing nearly all spurious edges while retaining nearly all true regulatory edges, resulting in highly accurate network reconstruction. In the most challenging scenario, the number of spurious edges is reduced from 239 to 0.
