File size: 2,259 Bytes
48097f5
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
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},
                  }  
                """