Title: Learning Explicit Single-Cell Dynamics Using ODE Representations
Primary area: unsupervised, self-supervised, semi-supervised, and supervised representation learning
Keywords: AI4Science, AI4Biology, gene interaction discovery, single-cell dynamics, dynamical systems

Abstract: Modeling the dynamics of cellular differentiation is fundamental to advancing the understanding and treatment of diseases associated with this process, such as cancer. With the rapid growth of single-cell datasets, this has also become a particularly promising and active domain for machine learning.
Current state-of-the-art models, however, rely on computationally expensive optimal transport preprocessing and multi-stage training, while also not discovering explicit gene interactions. 
To address these challenges we propose Cell-Mechanistic Neural Networks (*Cell-MNN*), an encoder-decoder architecture whose latent representation is a *locally linearized ODE* governing the dynamics of cellular evolution from stem to tissue cells. Cell-MNN is fully end-to-end (besides a standard PCA pre-processing) and its ODE representation explicitly learns biologically consistent and interpretable gene interactions.
Empirically, we show that Cell-MNN achieves competitive performance on single-cell benchmarks, surpasses state-of-the-art baselines in scaling to larger datasets and joint training across multiple datasets, while also learning interpretable gene interactions that we validate against the TRRUST database of gene interactions.

TL;DR: We propose Cell-MNN, an encoder–decoder model with a locally linearized latent ODE representation that achieves competitive performance on single-cell dynamics benchmarks, while discovering biologically consistent gene interactions.