A data-driven method to learn a jump diffusion process from aggregate biological gene expression data
From MaRDI portal
Publication:2243120
DOI10.1016/j.jtbi.2021.110923zbMath1476.92020OpenAlexW3127731322MaRDI QIDQ2243120
Michael Q. Zhang, Jia-Xing Gao, Zhenyi Wang, Da-quan Jiang, Min-ping Qian
Publication date: 10 November 2021
Published in: Journal of Theoretical Biology (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1101/2021.02.06.430082
Biochemistry, molecular biology (92C40) Developmental biology, pattern formation (92C15) Jump processes on discrete state spaces (60J74)
Related Items (2)
Analytical design of robust FO-PID controller for diffusion processes despite of uncertainty on all model parameters ⋮ Inference on the structure of gene regulatory networks
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Modelling biochemical reaction systems by stochastic differential equations with reflection
- A transformation approach to modelling multi-modal diffusions
- Multiscale stochastic modelling of gene expression
- The Onsager-Machlup function for diffusion processes
- The Onsager-Machlup function as Lagrangian for the most probable path of a diffusion process
- Limit theorems for generalized density-dependent Markov chains and bursty stochastic gene regulatory networks
- Effects of Lévy noise on the Fitzhugh-Nagumo model: a perspective on the maximal likely trajectories
- Mathematical physiology. I: Cellular physiology
- Lévy Processes and Stochastic Calculus
- Analysis and Data-Based Reconstruction of Complex Nonlinear Dynamical Systems
- The Onsager–Machlup function as Lagrangian for the most probable path of a jump-diffusion process
- Fluctuations and Irreversible Process. II. Systems with Kinetic Energy
- Parameter estimation for biochemical reaction networks using Wasserstein distances
This page was built for publication: A data-driven method to learn a jump diffusion process from aggregate biological gene expression data
