FunGO v2.0 backend

README.md

@@ -1,14 +1,41 @@
 ---
 title: FunGO
-emoji: 🐢
-colorFrom: indigo
-colorTo: purple
+emoji: 🧬
+colorFrom: blue
+colorTo: green
 sdk: gradio
-sdk_version: 6.12.0
+sdk_version: 4.44.0
 app_file: app.py
 pinned: false
 license: mit
-short_description: Multi-label protein function prediction via Gene Ontology
+short_description: Protein Function Prediction using ESM2 + XGBoost
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# FunGO — Protein Function Prediction
+
+**Beyond Prediction — Understanding Function.**
+
+FunGO predicts Gene Ontology (GO) terms for protein sequences using:
+- **ESM2-t36-3B** — protein language model embeddings (layers 30–35)
+- **XGBoost classifiers** — 4,133 GO-term specific models
+- **Evidence-tiered filtering** — Strong / Moderate / Indicative
+
+## Evidence Tiers
+
+| Tier | IA Weight | Confidence | Description |
+|------|-----------|------------|-------------|
+| Strong Evidence | > 5.0 | ≥ 0.30 | Highly specific GO term |
+| Moderate Evidence | > 2.0 | ≥ 0.50 | Moderately specific term |
+| Indicative | > 1.0 | ≥ 0.65 | Lower specificity, high confidence |
+
+## Ontologies Covered
+
+- **MFO** — Molecular Function
+- **BPO** — Biological Process  
+- **CCO** — Cellular Component
+
+## Development Team
+
+- **Dr. Beenish Maqsood** — Principal Investigator, School of Biochemistry and Biotechnology, University of the Punjab
+- **Dr. Naeem Mahmood** — Co-Supervisor, School of Biochemistry and Biotechnology, University of the Punjab
+- **Muteeba Azhar** — Lead Developer, MS Researcher, University of the Punjab

hf_README.md

@@ -1,41 +0,0 @@
----
-title: FunGO
-emoji: 🧬
-colorFrom: blue
-colorTo: green
-sdk: gradio
-sdk_version: 4.44.0
-app_file: app.py
-pinned: false
-license: mit
-short_description: Protein Function Prediction using ESM2 + XGBoost
----
-
-# FunGO — Protein Function Prediction
-
-**Beyond Prediction — Understanding Function.**
-
-FunGO predicts Gene Ontology (GO) terms for protein sequences using:
-- **ESM2-t36-3B** — protein language model embeddings (layers 30–35)
-- **XGBoost classifiers** — 4,133 GO-term specific models
-- **Evidence-tiered filtering** — Strong / Moderate / Indicative
-
-## Evidence Tiers
-
-| Tier | IA Weight | Confidence | Description |
-|------|-----------|------------|-------------|
-| Strong Evidence | > 5.0 | ≥ 0.30 | Highly specific GO term |
-| Moderate Evidence | > 2.0 | ≥ 0.50 | Moderately specific term |
-| Indicative | > 1.0 | ≥ 0.65 | Lower specificity, high confidence |
-
-## Ontologies Covered
-
-- **MFO** — Molecular Function
-- **BPO** — Biological Process  
-- **CCO** — Cellular Component
-
-## Development Team
-
-- **Dr. Beenish Maqsood** — Principal Investigator, School of Biochemistry and Biotechnology, University of the Punjab
-- **Dr. Naeem Mahmood** — Co-Supervisor, School of Biochemistry and Biotechnology, University of the Punjab
-- **Muteeba Azhar** — Lead Developer, MS Researcher, University of the Punjab