Update src/about.py

src/about.py

@@ -53,7 +53,7 @@ LLM_BENCHMARKS_TEXT = f"""
 
 ### PROBE is part of the the study entitled [Learning functional properties of proteins with language models](https://rdcu.be/cJAKN) which is schematically summarized in the figure below:<br/>
 
-![Summary of The Study](https://raw.githubusercontent.com/serbulent/TrainableRepresentationAnalysis/refs/heads/master/evalprotrep_summary_figure.jpg)
+![Summary of The Study](https://github-production-user-asset-6210df.s3.amazonaws.com/13165170/390718775-51176d93-85ac-4a24-92de-5d7c8334ca2e.jpg?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAVCODYLSA53PQK4ZA%2F20241128%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20241128T162659Z&X-Amz-Expires=300&X-Amz-Signature=21a96e9ba1c020054bdb91d53d467c6b256805ab11203279584c2140a343218e&X-Amz-SignedHeaders=host)
 
 ### If you find PROBE useful please consider citing!
 """
@@ -91,7 +91,10 @@ Welcome to the PROBE (Protein RepresentatiOn BEnchmark) leaderboard! This platfo
 
 Submit your own representation models and compare their performance across these tasks. For more details on how to participate, see the submission guidelines.
 
-If you find PROBE useful, please consider citing our work."""
+If you find PROBE useful, please consider citing our work:
+
+Unsal, S., Atas, H., Albayrak, M., Turhan, K., Acar, A. C., & Doğan, T. (2022). Learning functional properties of proteins with language models. *Nature Machine Intelligence, 4*(3), 227-245.
+"""
 
 similarity_tasks_options = ["sparse", "200", "500"]
 function_prediction_aspect_options = ["MF", "BP", "CC", "All_Aspects"]