ArticleJournal of the American Statistical Association2017
Network Reconstruction From High-Dimensional Ordinary Differential Equations.
Article in Journal of the American Statistical Association, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
- gNODE: gLV model-informed neural ordinary differential equations for modeling microbial community dynamics.Frontiers in cellular and infection microbiology · 2026Article
- Sepsis toxicity network reconstruction-Dynamic signaling and multi-organ injury: A review.Biomolecules & biomedicine · 2025Review
- Measuring Regulatory Network Inheritance in Dividing Yeast Cells Using Ordinary Differential Equations.bioRxiv : the preprint server for biology · 2025Article
- MODELING TRAJECTORIES USING FUNCTIONAL LINEAR DIFFERENTIAL EQUATIONS.The annals of applied statistics · 2024Article
- A hierarchical random effects state-space model for modeling brain activities from electroencephalogram data.Biometrics · 2024Article
- Data-driven Identification of Parametric Governing Equations of Dynamical Systems Using the Signed Cumulative Distribution Transform.Computer methods in applied mechanics and engineering · 2024Article
- Empirical Quantification of Predictive Uncertainty Due to Model Discrepancy by Training with an Ensemble of Experimental Designs: An Application to Ion Channel Kinetics.Bulletin of mathematical biology · 2023Article
- A latent state space model for estimating brain dynamics from electroencephalogram (EEG) data.Biometrics · 2023Article
- Gene regulatory network inference based on a nonhomogeneous dynamic Bayesian network model with an improved Markov Monte Carlo sampling.BMC bioinformatics · 2023Article
- A statistical framework for recovering pseudo-dynamic networks from static data.Bioinformatics (Oxford, England) · 2022Article
- A nonlinear sparse neural ordinary differential equation model for multiple functional processes.The Canadian journal of statistics = Revue canadienne de statistique · 2022Article
- Granger Causality: A Review and Recent Advances.Annual review of statistics and its application · 2022Article
- Kernel Ordinary Differential Equations.Journal of the American Statistical Association · 2022Article
- A Joint estimation approach to sparse additive ordinary differential equations.Statistics and computing · 2022Article
- Vaginal microbiota networks as a mechanistic predictor of aerobic vaginitis.Frontiers in microbiology · 2022Article
- Penalized estimation of threshold auto-regressive models with many components and thresholds.Electronic journal of statistics · 2022Article
- Joint Structural Break Detection and Parameter Estimation in High-Dimensional Non-Stationary VAR Models.Journal of the American Statistical Association · 2022Article
- Article
- Separable Nonlinear Least-Squares Parameter Estimation for Complex Dynamic Systems.Complexity · 2020Article
- Mixed-Effect Time-Varying Network Model and Application in Brain Connectivity Analysis.Journal of the American Statistical Association · 2020Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
We consider the task of learning a dynamical system from high-dimensional time-course data. For instance, we might wish to estimate a gene regulatory network from gene expression data measured at discrete time points. We model the dynamical system nonparametrically as a system of additive ordinary differential equations. Most existing methods for parameter estimation in ordinary differential equations estimate the derivatives from noisy observations. This is known to be challenging and inefficient. We propose a novel approach that does not involve derivative estimation. We show that the proposed method can consistently recover the true network structure even in high dimensions, and we demonstrate empirical improvement over competing approaches. Supplementary materials for this article are available online.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.