392-scDiffEq: Learning Cell Dynamics

Paper Talk · Jan 12, 2026 · 16m

The researchers introduce scDiffEq, a generative machine learning framework designed to model the complex drift and diffusion dynamics of single cells. Unlike previous tools that treat cellular randomness as a constant, this approach uses neural stochastic differential equations to capture how biological noise fluctuates across different cell states. By applying this method to lineage-traced sequencing data, the authors successfully reconstructed the developmental paths of blood cells and predicted their future identities with high accuracy. The framework also enables in silico perturbations…