From 52591f8a9f03869b748b22c160761202ba14c000 Mon Sep 17 00:00:00 2001
From: Yuta Nagano <52748151+yutanagano@users.noreply.github.com>
Date: Fri, 16 Aug 2024 18:35:16 +0900
Subject: [PATCH] Update README.md
- Fix logo url reference
- Improve title header formatting
- Add arXiv link badge
- Add citing section with BibTex
---
README.md | 20 +++++++++++++++++++-
1 file changed, 19 insertions(+), 1 deletion(-)
diff --git a/README.md b/README.md
index 1c98fe5..3f859ae 100644
--- a/README.md
+++ b/README.md
@@ -1,16 +1,19 @@
-

+

[](https://pypi.org/p/sceptr)

[](https://sceptr.readthedocs.io)
[](https://github.com/yutanagano/sceptr?tab=MIT-1-ov-file#readme)
+[](https://arxiv.org/abs/2406.06397v1)
### Check out the [documentation page](https://sceptr.readthedocs.io).
+---
+
**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.
@@ -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}
+}
+```