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--- |
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license: apache-2.0 |
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task_categories: |
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- feature-extraction |
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- text-classification |
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tags: |
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- protein |
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- bioinformatics |
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- embeddings |
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- conformal-prediction |
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size_categories: |
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- 1M<n<10M |
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--- |
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# Conformal Protein Retrieval - Data Files |
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This dataset contains the large data files required to run the [Conformal Protein Retrieval Gradio Space](https://huggingface.co/spaces/LoocasGoose/cpr). |
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## Contents |
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### 📊 Lookup Databases |
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**UniProt Database:** |
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- `data/lookup_embeddings.npy` - Pre-embedded UniProt protein sequences (Protein-Vec embeddings) |
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- `data/lookup_embeddings_meta_data.tsv` - Metadata for UniProt proteins (Entry, Pfam, Protein names) |
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**SCOPE Database:** |
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- `data/lookup/scope_lookup_embeddings.npy` - Pre-embedded SCOPE protein domain sequences |
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- `data/lookup/scope_lookup.fasta` - FASTA metadata for SCOPE proteins |
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### 🎯 Conformal Prediction Files |
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- `results/fdr_thresholds.csv` - Precomputed FDR (False Discovery Rate) thresholds |
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- `results/fnr_thresholds.csv` - Precomputed FNR (False Negative Rate) thresholds |
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- `results/calibration_probs.csv` - Calibration probabilities for Venn-Abers prediction |
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### 🧬 Protein-Vec Model |
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- `protein_vec_models/protein_vec.ckpt` - Main Protein-Vec model checkpoint |
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- `protein_vec_models/protein_vec_params.json` - Model configuration |
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- `protein_vec_models/*.py` - Model architecture code files |
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## Usage |
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These files are automatically loaded by the Gradio Space application. To use them locally: |
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```python |
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from huggingface_hub import hf_hub_download |
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import numpy as np |
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# Download a specific file |
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embedding_file = hf_hub_download( |
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repo_id="LoocasGoose/cpr_data", |
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filename="data/lookup_embeddings.npy", |
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repo_type="dataset" |
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) |
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# Load the embeddings |
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embeddings = np.load(embedding_file) |
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``` |
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## Citation |
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If you use these data files, please cite the original paper: |
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```bibtex |
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@article{boger2025functional, |
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title={Functional protein mining with conformal guarantees}, |
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author={Boger, Ron S and Chithrananda, Seyone and Angelopoulos, Anastasios N and Yoon, Peter H and Jordan, Michael I and Doudna, Jennifer A}, |
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journal={Nature Communications}, |
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volume={16}, |
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number={1}, |
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pages={85}, |
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year={2025}, |
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publisher={Nature Publishing Group UK London} |
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} |
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``` |
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## License |
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Apache 2.0 |
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## Source |
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Original data from: [Zenodo](https://zenodo.org/records/14272215) |
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