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# ChromBERT: A foundation model for learning interpretable representations for context-specific transcriptional regulatory networks 
[![Documentation](https://img.shields.io/badge/docs-available-brightgreen)](https://chrombert.readthedocs.io/en/)
[![License: GPL v3](https://img.shields.io/badge/License-GPLv3-blue.svg)](https://www.gnu.org/licenses/gpl-3.0)
[![DOI](https://zenodo.org/badge/767976655.svg)](https://doi.org/10.5281/zenodo.17824395)

**ChromBERT** is a pre-trained deep learning model designed to capture the genome-wide co-association patterns of approximately one thousand transcription regulators, thereby enabling accurate representations of context-specific transcriptional regulatory networks (TRNs). As a foundational model, ChromBERT can be fine-tuned to adapt to various biological contexts through transfer learning. This significantly enhances our understanding of transcription regulation and offers a powerful tool for a broad range of research and clinical applications in different biological settings.


<p align="center">
    <img src="https://raw.githubusercontent.com/zhaoweiyu-github/ChromBERT/refs/heads/main/docs/_static/ChromBERT_framework.png" width="80%"/>
<p>

This repository provides the checkpoints and required dependencies for ChromBERT.  
For usage, see [ChromBERT](https://github.com/TongjiZhanglab/ChromBERT) for detail.