# CFM-GP: Unified Conditional Flow Matching to Learn Gene Perturbation Across Cell Types

arXiv: https://arxiv.org/abs/2508.08312 · HTML: https://mlsanity.com/paper/2508.08312/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Flow matching, time series and science, one of 14 directions in Diffusion models.
- Builds on 5 of its 55 references that are in the map.
- First posted on arXiv 9 Aug 2025.

## Placed in

- Diffusion models / [Flow matching, time series and science](https://mlsanity.com/diffusion/flow-matching-time-series-and-science/)

## Builds on (most cited first, 5 of 5)

- [Flow Matching for Generative Modeling](https://mlsanity.com/paper/2210.02747/) (2022)
- [Modelling Cellular Perturbations with the Sparse Additive Mechanism Shift Variational Autoencoder](https://mlsanity.com/paper/2311.02794/) (2023)
- [Conditional out-of-sample generation for unpaired data using trVAE](https://mlsanity.com/paper/1910.01791/) (2019)
- [sc-OTGM: Single-Cell Perturbation Modeling by Solving Optimal Mass Transport on the Manifold of Gaussian Mixtures](https://mlsanity.com/paper/2405.03726/) (2024)
- [MoXGATE: Modality-aware cross-attention for multi-omic gastrointestinal cancer sub-type classification](https://mlsanity.com/paper/2506.06980/) (2025)

## Built on by (most cited first, 1 of 1)

- [Stable-Shift: Biologically Structured Prediction of Transcriptional Responses to Unseen Gene Perturbations](https://mlsanity.com/paper/2606.24940/) (2026)

## Abstract (arXiv)

Understanding gene perturbation effects across diverse cellular contexts is a central challenge in functional genomics, with important implications for therapeutic discovery and precision medicine. Single-cell technologies enable high-resolution measurement of transcriptional responses, but collecting such data is costly and time-consuming, especially when repeated for each cell type. Existing computational methods often require separate models per cell type, limiting scalability and generalization. We present CFM-GP, a method for cell type-agnostic gene perturbation prediction. CFM-GP learns a continuous, time-dependent transformation between unperturbed and perturbed gene expression distributions, conditioned on cell type, allowing a single model to predict across all cell types. Unlike prior approaches that use discrete modeling, CFM-GP employs a flow matching objective to capture perturbation dynamics in a scalable manner. We evaluate on five datasets: SARS-CoV-2 infection, IFN-beta stimulated PBMCs, glioblastoma treated with Panobinostat, lupus under IFN-beta stimulation, and Statefate progenitor fate mapping. CFM-GP consistently outperforms state-of-the-art baselines in R-squared and Spearman correlation, and pathway enrichment analysis confirms recovery of key biological pathways. These results demonstrate the robustness and biological fidelity of CFM-GP as a scalable solution for cross-cell type gene perturbation prediction.