Datasets:
Publish NapistuDataStore: .store
Browse files
README.md
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---
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tags: ['napistu', 'biological-networks', 'graph-neural-networks', 'napistu-data-store']
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library_name: napistu-torch
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license: mit
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---
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# NapistuDataStore Dataset
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This dataset contains a complete NapistuDataStore with all artifacts published as a read-only store.
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## Store Contents
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- **NapistuData artifacts**: 2
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- **VertexTensor artifacts**: 1
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- **pandas DataFrame artifacts**: 5
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- **Total artifacts**: 8
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## Artifacts
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### NapistuData
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- `relation_prediction`
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- `edge_prediction`
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### VertexTensors
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- `comprehensive_pathway_memberships`
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### pandas DataFrames
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- `edge_strata_by_node_species_type`
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- `edge_strata_by_edge_sbo_terms`
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- `species_identifiers`
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- `name_to_sid_map`
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- `edge_strata_by_node_type`
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## Usage
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### Load from HuggingFace Hub
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```python
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from huggingface_hub import hf_hub_download
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from napistu_torch.napistu_data_store import NapistuDataStore
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import tempfile
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import shutil
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# Download registry.json and create a temporary store directory
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with tempfile.TemporaryDirectory() as temp_dir:
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# Download registry.json
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registry_path = hf_hub_download(
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repo_id="username/repo-name",
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filename="registry.json",
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repo_type="dataset",
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local_dir=temp_dir
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)
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# Create store from downloaded registry
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store = NapistuDataStore(temp_dir)
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# Use the store (read-only)
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napistu_data = store.load_napistu_data("edge_prediction")
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```
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## Note
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This is a read-only store. The `sbml_dfs_path` and `napistu_graph_path` are set to None,
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so you cannot create new artifacts that require the raw SBML_dfs or NapistuGraph files.
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You can still load and use all existing artifacts.
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## Citation
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If you use this dataset, please cite:
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```bibtex
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@software{napistu_torch,
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title = {Napistu-Torch: Graph Neural Networks for Biological Pathway Analysis},
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author = {Hackett, Sean R.},
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url = {https://github.com/napistu/Napistu-Torch},
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year = {2025}
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}
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```
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## License
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MIT License - See [LICENSE](https://github.com/napistu/Napistu-Torch/blob/main/LICENSE) for details.
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