scDFM (ICLR 2026)
github.com/ai4science-westlakeu/scdfmDistributional flow matching model for robust single-cell perturbation prediction, modeling the full distribution of perturbed cellular expression profiles conditioned on control states via PAD-Transformer and multi-kernel MMD regularization; reduces MSE by 19.6% over the strongest baseline in combinatorial settings (Westlake University, 41+ stars, MIT License)
Sourced from
- Awesome AI for Science — github.com/ai4science-westlakeu/scdfm
- GitHub — github.com/ai4science-westlakeu/scdfm
Related resources
Single-cell analysis with transformers
Unified Python framework for bulk, single-cell, and spatial RNA-seq multi-omics analysis with deep learning deconvolution (VAE) and graph neural networks, bridging Bindea, Bindea, scanpy and squidpy ecosystems (Nature Communications 2024)
Automated cell type annotation tool for single-cell transcriptomics using gradient boosting and logistic regression with reference atlases, enabling standardized classification across datasets (Wellcome Sanger Institute, Nature Biotechnology 2022)
Xaira-Therapeutics/X-Cell
by Xaira-TherapeuticsA diffusion language model for genome-scale perturbation prediction across diverse cellular contexts.
marvinsxtr/MapPFN
by marvinsxtrPre-trained and fine-tuned checkpoints for MapPFN: Learning Causal Perturbation Maps in Context (Sextro et al., 2026).