model: graph_conv-rotate_h128_l3_edge_prediction | (graph_conv-rotate_h128_l3) | WandB: xpa2bgz2

README.md

@@ -0,0 +1,150 @@
+---
+tags: ['napistu', 'napistu-torch', 'graph-neural-networks', 'biological-networks', 'pytorch', 'graph_conv', 'rotate', 'edge_prediction', 'relation-aware']
+library_name: napistu-torch
+license: mit
+metrics:
+- auc
+- average_precision
+---
+
+# graph_conv-rotate_h128_l3_edge_prediction
+
+This model was trained using [Napistu-Torch](https://www.shackett.org/napistu_torch/), a PyTorch framework for training graph neural networks on biological pathway networks.
+
+The dataset used for training is the 8-source ["Octopus" human consensus network](https://www.shackett.org/octopus_network/), which integrates pathway data from STRING, OmniPath, Reactome, and others. The network encompasses ~50K genes, metabolites, and complexes connected by ~8M interactions.
+
+## Task
+
+This model performs **edge prediction** on biological pathway networks. Given node embeddings, 
+the model predicts the likelihood of edges (interactions) between biological entities such as 
+genes, proteins, and metabolites. This is useful for:
+
+- Discovering novel biological interactions
+- Validating experimentally observed interactions
+- Completing incomplete pathway databases
+- Predicting functional relationships between genes/proteins
+
+The model learns to score potential edges based on learned embeddings of source and target nodes, 
+optionally incorporating relation types for relation-aware prediction.
+
+## Model Description
+
+- **Encoder**
+  - Type: `graph_conv`
+  - Hidden Channels: `128`
+  - Number of Layers: `3`
+  - Dropout: `0.2`
+  - Edge Encoder: ✓ (dim=32)
+- **Head**
+  - Type: `rotate`
+  - Relation-Aware: ✓
+
+**Training Date**: 2026-01-01
+
+For detailed experiment and training settings see this repository's `config.json` file.
+
+## Performance
+
+| Metric | Value |
+|--------|-------|
+| Validation relation-weighted AUC | 0.8105 |
+| Test relation-weighted AUC | 0.8193 |
+| Validation AUC | 0.7859 |
+| Test AUC | 0.8089 |
+| Validation AP | 0.7918 |
+| Test AP | 0.8126 |
+
+
+## Links
+
+- 📊 [W&B Run](https://wandb.ai/napistu/napistu-experiments/runs/xpa2bgz2)
+- 🌐 [Napistu](https://napistu.com)
+- 💻 [GitHub Repository](https://github.com/napistu/Napistu-Torch)
+- 📖 [Read the Docs](https://napistu-torch.readthedocs.io/en/latest)
+- 📚 [Napistu Wiki](https://github.com/napistu/napistu/wiki)
+
+## Usage
+
+### 1. Setup Environment
+
+To reproduce the environment used for training, run the following commands:
+
+```bash
+pip install torch==2.8.0
+pip install torch-scatter torch-sparse -f https://data.pyg.org/whl/2.8.0+cpu.html
+pip install 'napistu==0.8.5'
+pip install 'napistu-torch[pyg,lightning]==0.3.4'
+```
+
+### 2. Setup Data Store
+
+First, download the Octopus consensus network data to create a local `NapistuDataStore`:
+```python
+from napistu_torch.load.gcs import gcs_model_to_store
+
+# Download data and create store
+napistu_data_store = gcs_model_to_store(
+    napistu_data_dir="path/to/napistu_data",
+    store_dir="path/to/store",
+    asset_name="human_consensus",
+    # Pin to stable version for reproducibility
+    asset_version="20250923"
+)
+```
+
+### 3. Load Pretrained Model from HuggingFace Hub
+```python
+from napistu_torch.ml.hugging_face import HFModelLoader
+
+# Load checkpoint
+loader = HFModelLoader("seanhacks/relation_prediction_rotate_128e")
+checkpoint = loader.load_checkpoint()
+
+# Load config to reproduce experiment
+experiment_config = loader.load_config()
+```
+
+### 4. Use Pretrained Model for Training
+
+You can use this pretrained model as initialization for training via the CLI:
+```bash
+# Create a training config that uses the pretrained model
+cat > my_config.yaml << EOF
+name: my_finetuned_model
+
+model:
+  use_pretrained_model: true
+  pretrained_model_source: huggingface
+  pretrained_model_path: seanhacks/relation_prediction_rotate_128e
+  pretrained_model_freeze_encoder_weights: false  # Allow fine-tuning
+
+data:
+  sbml_dfs_path: path/to/sbml_dfs.pkl
+  napistu_graph_path: path/to/graph.pkl
+  napistu_data_name: edge_prediction
+
+training:
+  epochs: 100
+  lr: 0.001
+EOF
+
+# Train with pretrained weights
+napistu-torch train my_config.yaml
+```
+
+## Citation
+
+If you use this model, please cite:
+```bibtex
+@software{napistu_torch,
+  title = {Napistu-Torch: Graph Neural Networks for Biological Pathway Analysis},
+  author = {Hackett, Sean R.},
+  url = {https://github.com/napistu/Napistu-Torch},
+  year = {2025},
+  note = {Model: graph_conv-rotate_h128_l3_edge_prediction}
+}
+```
+
+## License
+
+MIT License - See [LICENSE](https://github.com/napistu/Napistu-Torch/blob/main/LICENSE) for details.