Update README.md

README.md

@@ -6,52 +6,60 @@ pretty_name: ProtHGT Knowledge Graph Data & Pretrained Checkpoints
 ---
 
 # ProtHGT Knowledge Graph Data & Pretrained Checkpoints
-This repository provides the **knowledge graph (KG) `.pt` files** and **pretrained model checkpoints** used in **ProtHGT: Heterogeneous Graph Transformers for Automated Protein Function Prediction Using Biological Knowledge Graphs and Language Models**.
+This repository provides the **knowledge graph (KG) `.pt` files** and **pretrained model checkpoints** used in **ProtHGT: Heterogeneous Graph Transformers for Automated Protein Function Prediction Using Knowledge Graphs and Language Models**.
 - **Code (training & prediction)**: https://github.com/HUBioDataLab/ProtHGT
 
 ---
 
 ## What’s Inside
+Files are organized by task: `go/` for GO term prediction and `ec/` for EC number prediction, 
+under both `knowledge_graphs/` and `models/`. In each task, the top-level files use the default TAPE protein embeddings, 
+and `alternative_protein_embeddings/` holds versions whose protein node features use ESM-2 or ProtT5 instead.
 
-### data/
-PyTorch Geometric-compatible KG files:
-- Full KG file (e.g., `prothgt-kg.pt`)
-- Train/validation/test splits (e.g., `prothgt-*-graph.pt`)
-- Alternative KG versions under `alternative_protein_embeddings/` (e.g., `esm2/`, `prott5/`), where the protein node features differ by embedding type.
+### knowledge_graphs/
+PyTorch Geometric-compatible KG files: a full KG plus train/validation/test splits per task.
 
-**Available Files**
+**GO term prediction — `knowledge_graphs/go/`**
 ```
-├── prothgt-kg.pt                      # The default full knowledge graph containing TAPE embeddings as the initial protein representations.
-├── prothgt-train-graph.pt             # Training set (80% of the default full KG).
-├── prothgt-val-graph.pt               # Validation set (10% of the default full KG).
-├── prothgt-test-graph.pt              # Test set (10% of the default full KG).
-└── alternative_protein_embeddings/    # Contains alternative KGs with different protein representations.
-    ├──apaac/
-    │   └── ...
-    ├──esm2/
-    │   └── ...
-    └──prott5/
-        └── ...
+├── prothgt-kg.pt                      # The default full knowledge graph (TAPE embeddings).
+├── prothgt-train-graph.pt             # Training set (80% of the full KG).
+├── prothgt-val-graph.pt               # Validation set (10%).
+├── prothgt-test-graph.pt              # Test set (10%).
+└── alternative_protein_embeddings/    # ESM-2 / ProtT5 variants.
+    ├── esm2/   prothgt-esm2-{kg,train-graph,val-graph,test-graph}.pt
+    └── prott5/ prothgt-prott5-{kg,train-graph,val-graph,test-graph}.pt
 ```
 
+**EC number prediction — `knowledge_graphs/ec/`**
+```
+├── prothgt-ec-kg.pt                   # The default full EC knowledge graph (TAPE embeddings).
+├── prothgt-ec-train-graph.pt          # Training set.
+├── prothgt-ec-val-graph.pt            # Validation set.
+├── prothgt-ec-test-graph.pt           # Test set (transductive: training proteins' EC edges injected).
+└── alternative_protein_embeddings/    # ESM-2 / ProtT5 variants.
+    ├── esm2/   prothgt-ec-esm2-{kg,train-graph,val-graph,test-graph}.pt
+    └── prott5/ prothgt-ec-prott5-{kg,train-graph,val-graph,test-graph}.pt
+```
 
 ### models/
-Pretrained ProtHGT models (`.pt`). Models are provided:
-- per GO sub-ontology (e.g., Molecular Function / Biological Process / Cellular Component)
-- per protein embedding type (default vs `esm2` / `prott5` / etc.)
+Pretrained ProtHGT checkpoints (`.pt`). **Use a checkpoint that matches the KG embedding variant you are using.**
 
-**Important:** Use a model checkpoint that matches the KG embedding variant you are using.
+**GO term prediction — `models/go/`** (one model per GO sub-ontology × embedding)
+```
+├── prothgt-model-molecular-function.pt      # default TAPE-based KG
+├── prothgt-model-biological-process.pt
+├── prothgt-model-cellular-component.pt
+└── alternative_protein_embeddings/          # ESM-2 / ProtT5 variants.
+    ├── esm2/   prothgt-esm2-model-{molecular-function,biological-process,cellular-component}.pt
+    └── prott5/ prothgt-prott5-model-{molecular-function,biological-process,cellular-component}.pt
+```
 
-**Available Files**
+**EC number prediction — `models/ec/`** (one joint hierarchical model per embedding, predicting all four EC levels)
 ```
-├── prothgt-model-molecular-function.pt      # Pretrained ProtHGT checkpoint for Molecular Function (default/TAPE-based KG).
-├── prothgt-model-biological-process.pt      # Pretrained ProtHGT checkpoint for Biological Process (default/TAPE-based KG).
-├── prothgt-model-cellular-component.pt      # Pretrained ProtHGT checkpoint for Cellular Component (default/TAPE-based KG).
-└── alternative_protein_embeddings/          # Models trained with alternative protein representations.
-    ├── esm2/
-    │   └── ...
-    └── prott5/
-        └── ...
+├── prothgt-ec-model.pt                      # default TAPE-based KG
+└── alternative_protein_embeddings/          # ESM-2 / ProtT5 variants.
+    ├── esm2/   prothgt-ec-esm2-model.pt
+    └── prott5/ prothgt-ec-prott5-model.pt
 ```
 
 ---
@@ -63,6 +71,8 @@ Key scripts:
 - `train.py` — trains ProtHGT using the provided KG splits
 - `predict.py` — runs inference using pretrained checkpoints
 
+GO term prediction lives at the top level of `src/`; EC number prediction is in `src/ec/`.
+
 ---
 
 ### Citation