README.md

---
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.

[![GitHub](https://img.shields.io/badge/GitHub-ZjGaothu%2FRegFM-181717?logo=github)](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