A physics-informed neural SDE network for learning cellular dynamics from time-series scRNA-seq data.

ABSTRACT:

Motivation

Learning cellular dynamics through reconstruction of the underlying cellular potential energy landscape (aka Waddington landscape) from time-series single-cell RNA sequencing (scRNA-seq) data is a current challenge. Prevailing data-driven computational methods can be hampered by the lack of physical principles to guide learning from complex data, resulting in reduced prediction accuracy and interpretability when applied to infer cell population dynamics.

Results

Here, we propose PI-SDE, a physics-informed neural stochastic differential equation (SDE) framework that combines the Hamilton-Jacobi (HJ) equation and neural SDE to learn cellular dynamics. Grounded in potential energy theory of biological systems, PI-SDE integrates the principle of least action by enforc

SUBMITTER: Jiang Q 

PROVIDER: S-EPMC11373338 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature