---
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)