ArticleNature communications2024
Learning interpretable dynamics of stochastic complex systems from experimental data.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed.
- Discovering multiscale deep formulas in complex systems via neural-guided lambda calculus.Nature communications · 2026Article
- Statistical learning of stochastic complex systems via the Yau-Yau nonlinear filter.Innovation (Cambridge (Mass.)) · 2026Article
- Encoding Cumulation to Learn Perturbative Nonlinear Oscillatory Dynamics.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Intelligent regulation of university faculty interdisciplinary collaboration networks based on complex network topology evolution and stochastic differential equations.Scientific reports · 2025Article
- Privacy preserving optimization of communication networks.Nature communications · 2025Article
- Emergence of cooperation promoted by higher-order strategy updates.PLoS computational biology · 2025Article
- Learning interpretable network dynamics via universal neural symbolic regression.Nature communications · 2025Article
- Learning interpretable dynamics of stochastic complex systems from experimental data.Nature communications · 2024Article
- Data-Driven Identification of Rational Nonlinear Dynamics in Biochemical Networks via an Implicit Singular Value Decomposition Based Framework.IET systems biologyArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Complex systems with many interacting nodes are inherently stochastic and best described by stochastic differential equations. Despite increasing observation data, inferring these equations from empirical data remains challenging. Here, we propose the Langevin graph network approach to learn the hidden stochastic differential equations of complex networked systems, outperforming five state-of-the-art methods. We apply our approach to two real systems: bird flock movement and tau pathology diffusion in brains. The inferred equation for bird flocks closely resembles the second-order Vicsek model, providing unprecedented evidence that the Vicsek model captures genuine flocking dynamics. Moreover, our approach uncovers the governing equation for the spread of abnormal tau proteins in mouse brains, enabling early prediction of tau occupation in each brain region and revealing distinct pathology dynamics in mutant mice. By learning interpretable stochastic dynamics of complex systems, our findings open new avenues for downstream applications such as control.
Indexed as
Identifiers
What OpenQuestion holds
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.