BiologyNature Communications
Learning interpretable dynamics of stochastic complex systems from experimental data
T. Gao, B. Barzel, et al.
Discover how the Langevin Graph Network Approach (LaGNA) revolutionizes the inference of stochastic differential equations from empirical data for complex networks. Developed by Ting-Ting Gao, Baruch Barzel, and Gang Yan, this innovative method outshines existing techniques, providing critical insights into bird flock dynamics and tau pathology in mice.
Related Publications
Explore these studies to deepen your understanding
Adjacent work that informs or extends this paper's methodology and findings.
Chemistry
Extracting structural motifs from pair distribution function data of nanostructures using explainable machine learning
A. S. Anker, E. T. S. Kjær, et al.
Medicine and Health
Unsupervised learning of aging principles from longitudinal data
K. Avchaciov, M. P. Antoch, et al.
Medicine and Health
Deep learning from "passive feeding" to "selective eating" of real-world data
Z. Li, C. Guo, et al.
Computer Science
Machine learning dismantling and early-warning signals of disintegration in complex systems
M. Grassia, M. D. Domenico, et al.
