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README.md

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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.
+
+[![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