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README.md
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[](https://doi.org/10.5281/zenodo.17824395)
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**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.
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This repository provides the checkpoints and required dependencies for ChromBERT.
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For usage, see [ChromBERT](https://github.com/TongjiZhanglab/ChromBERT) for detail.
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[](https://doi.org/10.5281/zenodo.17824395)
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**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.
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<img src="https://raw.githubusercontent.com/zhaoweiyu-github/ChromBERT/refs/heads/main/docs/_static/ChromBERT_framework.png" width="80%"/>
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This repository provides the checkpoints and required dependencies for ChromBERT.
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For usage, see [ChromBERT](https://github.com/TongjiZhanglab/ChromBERT) for detail.
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