{"ID":2860900,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.02903","arxiv_id":"2510.02903","title":"Learning Explicit Single-Cell Dynamics Using ODE Representations","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.","short_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-th...","url_abs":"https://arxiv.org/abs/2510.02903","url_pdf":"https://arxiv.org/pdf/2510.02903v2","authors":"[\"Jan-Philipp von Bassewitz\",\"Adeel Pervez\",\"Marco Fumero\",\"Matthew Robinson\",\"Theofanis Karaletsos\",\"Francesco Locatello\"]","published":"2025-10-03T11:15:16Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"q-bio.CB\"]","methods":"[]","has_code":false}