README.md

---
license: mit
task_categories:
- text-classification
tags:
- peptide
- mhc
- hla
- binding-prediction
- immunology
size_categories:
- n>1M
---

# PM (Peptide-MHC) Binding Prediction Dataset

## Dataset Description

This dataset is part of the SPRINT benchmark framework for TCR-pMHC binding prediction. It contains peptide-MHC binding data for training and in-distribution testing of binding prediction models.

### Dataset Summary

The PM dataset focuses on peptide-MHC binding prediction without TCR information. It is reorganized and standardized from multiple sources to provide a clean benchmark for PM task evaluation.

## Dataset Structure

### Files

- `train.csv`: Training data
- `id_test.csv`: In-distribution test data

### Data Format

Each CSV file contains the following columns:

| Column | Type | Description |
|--------|------|-------------|
| peptide | string | Peptide amino acid sequence |
| HLA | string | HLA allele (standardized format: A*02:01) |
| label | int | Binary binding label (0=non-binder, 1=binder) |
| length | int | Peptide length (8-14 amino acids) |
| HLA_sequence | string | HLA pseudo-sequence |

### Dataset Statistics

**Training Set:**
- Total samples: 1683280
- Label type: Binary (0/1)
- Positive rate: 18.25% (307201/1683280)
- Unique HLAs: 112
- Unique peptides: 1481879
- Peptide length range: 8-14

**Test Set (In-Distribution):**
- Total samples: 586608
- Label type: Binary (0/1)
- Positive rate: 14.64% (85876/586608)
- Unique HLAs: 112
- Unique peptides: 558035
- Peptide length range: 8-14

## Usage

### Load with Pandas

```python
from huggingface_hub import hf_hub_download
import pandas as pd

# Download files
train_file = hf_hub_download(
    repo_id="YYJMAY/pm-binding",
    filename="train.csv",
    repo_type="dataset"
)
test_file = hf_hub_download(
    repo_id="YYJMAY/pm-binding",
    filename="id_test.csv",
    repo_type="dataset"
)

# Load data
train_df = pd.read_csv(train_file)
test_df = pd.read_csv(test_file)
```

### Use with SPRINT Framework

```python
from sprint.core.dataset_manager import DatasetManager

manager = DatasetManager()
config = {
    'hf_repo': 'YYJMAY/pm-binding',
    'files': ['train.csv', 'id_test.csv']
}

files = manager.get_dataset('pm', config)
```

## Data Preparation

This dataset was prepared with the following steps:

1. **Source Integration**: Combined data from multiple PM binding datasets
2. **HLA Standardization**: Normalized HLA allele names to A*02:01 format
3. **Quality Control**: Removed duplicates and incomplete entries
4. **Column Standardization**: Unified column names and formats
5. **Validation**: Checked for data consistency and quality

## Tasks

This dataset is designed for:

- **Peptide-MHC Binding Prediction**: Predicting binding affinity between peptides and MHC molecules
- **In-Distribution Evaluation**: Testing model performance on similar data distribution as training
- **Baseline Comparison**: Standardized data for reproducible benchmarking

## Citation

If you use this dataset, please cite:

```bibtex
@dataset{pm_dataset_2024,
  title={PM (Peptide-MHC) Binding Prediction Dataset},
  author={SPRINT Framework Contributors},
  year={2024},
  url={https://huggingface.co/datasets/YYJMAY/pm-binding}
}
```

## License

MIT License

## Contact

For questions or issues, please refer to the SPRINT framework repository.