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Erva Ulusoy commited on Feb 3

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867722f

1 Parent(s): 4190d38

added new future work point

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ProtHGT_app.py +2 -1

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@@ -46,7 +46,8 @@ with st.expander("🚀 Upcoming Features"):
     - **Real-time data retrieval for new proteins**: Currently, ProtHGT can only generate predictions for proteins that already exist in our knowledge graph. We are developing a new feature that will allow users to **predict functions for entirely new proteins starting from their sequences**. This will work by **retrieving relevant relationship data in real time from external source databases** (e.g., UniProt, STRING, and other biological repositories). The system will dynamically construct a knowledge graph for the query protein, incorporating its interactions, domains, pathways, and other biological associations before running function prediction. This approach will enable ProtHGT to analyze newly discovered or less-studied proteins even if they are not pre-annotated in our dataset.
     - **Expanded embedding options**: Currently, this application represents proteins using **TAPE embeddings**, which serve as the initial numerical representations of protein sequences before being processed in the heterogeneous graph model. We are working on integrating **ProtT5** and **ESM-2** as alternative initial embeddings, allowing users to choose different sequence representations that may enhance performance for specific tasks. A detailed comparison of how these embeddings influence function prediction accuracy will be included in our upcoming publication.
     Stay tuned for updates and future publications!
     """)

     - **Real-time data retrieval for new proteins**: Currently, ProtHGT can only generate predictions for proteins that already exist in our knowledge graph. We are developing a new feature that will allow users to **predict functions for entirely new proteins starting from their sequences**. This will work by **retrieving relevant relationship data in real time from external source databases** (e.g., UniProt, STRING, and other biological repositories). The system will dynamically construct a knowledge graph for the query protein, incorporating its interactions, domains, pathways, and other biological associations before running function prediction. This approach will enable ProtHGT to analyze newly discovered or less-studied proteins even if they are not pre-annotated in our dataset.
     - **Expanded embedding options**: Currently, this application represents proteins using **TAPE embeddings**, which serve as the initial numerical representations of protein sequences before being processed in the heterogeneous graph model. We are working on integrating **ProtT5** and **ESM-2** as alternative initial embeddings, allowing users to choose different sequence representations that may enhance performance for specific tasks. A detailed comparison of how these embeddings influence function prediction accuracy will be included in our upcoming publication.
+    - **Knowledge graph visualization for interpretability**: To improve model explainability, we are developing an interactive **knowledge graph visualization** feature. This will allow users to explore the biological relationships that contributed to ProtHGT’s predictions for a given protein. Users will be able to inspect **protein interactions, GO annotations, domains, pathways, and other key connections** in a structured graphical format, making it easier to interpret and validate predictions.
     Stay tuned for updates and future publications!
     """)