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info_text = ("""
<div style="text-align: justify">
This application enables to inspect mutational effects on a
predefined protein sequence.<br>
<br>
</div>
<div style="text-align: justify">
It is built on the Prot-xLSTM backbone model, an xLSTM model specifically
trained on protein sequences. Prot-xLSTM was trained using the
Fill-In-the-Middle (FIM) objective, which allows it to perform sequence
inpainting. Additionally, the model can be provided with a potentially
large set of homologous sequences to enhance its predictions.<br>
<br>
</div>
<div style="text-align: justify">
For further information please refer, to: <a href="https://openreview.net/forum?id=IjbXZdugdj" target="_blank">https://openreview.net/forum?id=IjbXZdugdj</a>. <br>
<br>
This Hugging Face application is based on the following GitHub repository:
<a href="https://github.com/ml-jku/Prot-xLSTM?tab=readme-ov-file" target="_blank">https://github.com/ml-jku/Prot-xLSTM?tab=readme-ov-file</a>. <br>
The streamlit application was developed by Elias Bürger.
</div>
<div style="text-align: justify">
Please cite us as follows: <br>
</div>
""")
citation_text = """
@misc{
schmidinger2024bioxlstmgenerativemodelingrepresentation,
title={Bio-xLSTM: Generative modeling, representation and in-context learning of biological and chemical sequences},
author={Niklas Schmidinger and Lisa Schneckenreiter and Philipp Seidl and Johannes Schimunek and Pieter-Jan Hoedt and Johannes Brandstetter and Andreas Mayr and Sohvi Luukkonen and Sepp Hochreiter and Günter Klambauer},
year={2024},
eprint={2411.04165},
archivePrefix={arXiv},
primaryClass={q-bio.BM},
url={https://arxiv.org/abs/2411.04165},
}
""" |