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Update README.md
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- Fix logo url reference
- Improve title header formatting
- Add arXiv link badge
- Add citing section with BibTex
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yutanagano authored Aug 16, 2024
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<div align="center">

<img src="https://raw.githubusercontent.com/yutanagano/tidytcells/main/sceptr.svg" width=700>
<img src="https://raw.githubusercontent.com/yutanagano/sceptr/main/sceptr.svg" width=700>

[![Latest release](https://img.shields.io/pypi/v/sceptr)](https://pypi.org/p/sceptr)
![Tests](https://github.com/yutanagano/sceptr/actions/workflows/tests.yaml/badge.svg)
[![Documentation Status](https://readthedocs.org/projects/sceptr/badge/?version=latest)](https://sceptr.readthedocs.io)
[![License](https://img.shields.io/badge/license-MIT-blue)](https://github.com/yutanagano/sceptr?tab=MIT-1-ov-file#readme)
[![arXiv](https://img.shields.io/badge/arXiv-arXiv:2406.06397-pink)](https://arxiv.org/abs/2406.06397v1)

### Check out the [documentation page](https://sceptr.readthedocs.io).

</div>

---

**SCEPTR** (**S**imple **C**ontrastive **E**mbedding of the **P**rimary sequence of **T** cell **R**eceptors) is a small, fast, and accurate TCR representation model that can be used for alignment-free TCR analysis, including for TCR-pMHC interaction prediction and TCR clustering (metaclonotype discovery).
Our [preprint](https://arxiv.org/abs/2406.06397) demonstrates that SCEPTR can be used for few-shot TCR specificity prediction with improved accuracy over previous methods.

Expand All @@ -26,3 +29,18 @@ What's even better is that they are fully compliant with [pyrepseq](https://pyre
```bash
pip install sceptr
```

## Citing SCEPTR
Please cite our [preprint](https://arxiv.org/abs/2406.06397).

### BibTex
```bibtex
@misc{nagano2024contrastive,
title={Contrastive learning of T cell receptor representations},
author={Yuta Nagano and Andrew Pyo and Martina Milighetti and James Henderson and John Shawe-Taylor and Benny Chain and Andreas Tiffeau-Mayer},
year={2024},
eprint={2406.06397},
archivePrefix={arXiv},
primaryClass={q-bio.BM}
}
```

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