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license: mit
library_name: pytorch
pipeline_tag: other
tags:
- genomics
- gene-expression
- transcriptional-regulation
language:
- en
pretty_name: RegFM
---
# RegFM
**RegFM** is a context-aware foundation model for human transcriptional regulation.
It treats regulation as a dialogue between **cis-regulatory sequences (CREs)** and **trans-acting regulators** (transcription factors and chromatin regulators), coupling long-range CRE representations with TF/CR activity.
Trained on large-scale ENCODE and CELLxGENE transcriptomic profiles, RegFM learns gene-centered regulatory representations that generalize across unseen cellular contexts.
[](https://github.com/ZjGaothu/RegFM)
<p align="center">
<img src="init/model.jpg" width="85%" alt="RegFM overview">
</p>
## Model description
- **Inputs**: long-range cis-DNA sequence features + cell-context TF/CR and expression signals
- **Outputs**: gene expression predictions and regulatory representations usable for downstream tasks
- **Framework**: PyTorch
## Intended uses
- Gene expression prediction in unseen cellular contexts
- Cis-regulatory element annotation
- Bivalent promoter / dosage-sensitivity related analyses
- Perturbation-response prediction
- Interpretable analysis of cis–trans regulatory interactions
## Code & demo
Code, training/prediction scripts, and a PBMC leave-one-out demo (predict on held-out **CD8 TEM 1**) live on GitHub:
**https://github.com/ZjGaothu/RegFM**
```bash
pip install git+https://github.com/ZjGaothu/RegFM.git
# or clone and: pip install -e .
```
## Citation
If you use RegFM, please cite:
Zijing Gao, et al. RegFM: an interpretable context-aware foundation model for human transcriptional regulation. bioRxiv, (2026).
*(DOI will be added upon public release.)*
## Contact
`gzj21@mails.tsinghua.edu.cn`
## License
MIT
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