ArticleBioinformatics (Oxford, England)2025
Reconstructing gene network structure and dynamics from single cell data.
Article in Bioinformatics (Oxford, England), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Joint inference of paired dynamical gene regulatory networks reveals distinct cell-state landscapes of neutrophil reprogramming.bioRxiv : the preprint server for biology · 2026Article
- Learning collective multicellular dynamics with an interacting mean field neural SDE model.PLoS computational biology · 2026Article
- Building dynamical models of multi-step state transitions from single cell gene expression trajectories.bioRxiv : the preprint server for biology · 2025Article
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Authors and funding
2 authors.
Funding
Abstract
motivationBiological functions are governed by gene regulatory networks (GRNs). Accurately inferring GRNs from high-dimensional and noisy single-cell data remains a major challenge in systems biology. Conventional approaches often struggle with robustness and interpretability, particularly when applied to complex biological processes such as cell fate decisions and complex diseases.
resultsIn this study, we propose GGANO, a hybrid framework that integrates Gaussian Graphical Models for conditional independence learning with Neural Ordinary Differential Equations for dynamic modeling and inference. Benchmark analyzes show that GGANO achieves superior accuracy and stability compared to existing methods, particularly under high-noise conditions. Furthermore, GGANO enables the inference of stochastic dynamics from single-cell data. Applying GGANO to the EMT datasets, we uncover intermediate cellular states and key regulatory genes driving EMT progression. AVAILABILITY AND IMPLEMENTATION: The source code is available at GitHub: https://github.com/ChenFeng87/GGANO.
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Registered trials
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