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