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README.md

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+---
+license: mit
+task_categories:
+- text-classification
+tags:
+- mhc
+- peptide
+- immunology
+- out-of-distribution
+- modality-shift
+- binding-affinity
+- eluted-ligand
+size_categories:
+- 1M<n<10M
+---
+
+# Modality OOD Dataset
+
+## Dataset Description
+
+The Modality OOD dataset tests model generalization across **different data modalities** in peptide-MHC (pMHC) binding prediction. It contains two complementary datasets representing distinct experimental measurement types:
+
+- **BA (Binding Affinity)**: In vitro binding affinity measurements with continuous values
+- **EL (Eluted Ligand)**: Mass spectrometry-based eluted ligand data with binary labels
+
+### Key Features
+
+- **Modality Shift Testing**: Evaluates if models trained on one modality (e.g., BA) can generalize to another (e.g., EL)
+- **Real-World Relevance**: Reflects the practical challenge of applying models across different experimental platforms
+- **Large Scale**: Combined 3.85M samples across 130+ HLA alleles
+- **Single Allele Format**: Each sample has one peptide-HLA pair (no multi-allele)
+
+### Biological Significance
+
+**Why Two Modalities Matter:**
+
+1. **Binding Affinity (BA)**:
+   - Measures peptide-MHC binding strength in controlled conditions
+   - Continuous scale (0 = no binding, 1 = strong binding)
+   - Reflects thermodynamic stability
+   - Common in immunoinformatics training data
+
+2. **Eluted Ligand (EL)**:
+   - Peptides naturally presented on cell surface MHC molecules
+   - Binary label (1 = naturally presented, 0 = not presented)
+   - Reflects cellular processing, TAP transport, and MHC loading
+   - More biologically relevant but harder to obtain
+
+**The Modality Gap:**
+Models trained on BA data often fail on EL data (and vice versa) because:
+- BA measures binding only, EL captures the full antigen processing pathway
+- Different experimental biases and noise profiles
+- Label semantics differ (affinity vs. presentation)
+
+This dataset enables testing cross-modality generalization.
+
+## Dataset Structure
+
+### Files
+
+- **ba_s.csv**: Binding Affinity dataset (single allele)
+- **el_s.csv**: Eluted Ligand dataset (single allele)
+
+### Data Format
+
+Both files share the same schema:
+
+| Column | Type | Description | Required |
+|--------|------|-------------|----------|
+| peptide | string | Peptide amino acid sequence (8-15aa) | Yes |
+| HLA | string | HLA allele (e.g., HLA-A02:01, HLA-B07:02) | Yes |
+| label | float/int | BA: continuous 0-1, EL: binary 0/1 | Yes |
+| HLA_sequence | string | HLA pseudo-sequence | Yes |
+
+### Dataset Statistics
+
+#### BA (Binding Affinity)
+
+- **Total Samples**: 170,470
+- **Label Type**: Continuous (0.0 to 1.0)
+- **Mean Affinity**: 0.2547
+- **Median Affinity**: 0.0847
+- **Unique HLA Alleles**: 111
+- **Peptide Lengths**: 8-14 amino acids (74.3% are 9-mers)
+- **File Size**: 10.61 MB
+
+#### EL (Eluted Ligand)
+
+- **Total Samples**: 3,679,405
+- **Label Type**: Binary classification
+- **Positive Samples**: 197,547 (5.4%)
+- **Negative Samples**: 3,481,858 (94.6%)
+- **Unique HLA Alleles**: 130
+- **Peptide Lengths**: 8-15 amino acids (distributed across all lengths)
+- **File Size**: 213.35 MB
+
+### Combined Statistics
+
+- **Total Samples**: 3,849,875
+- **Unique HLA Coverage**: 130+ alleles across HLA-A, B, C
+- **Modalities**: 2 (BA and EL)
+- **Task Type**: Peptide-MHC (PM) binding prediction
+
+## Usage
+
+### Load with Pandas
+
+```python
+from huggingface_hub import hf_hub_download
+import pandas as pd
+
+# Download BA dataset
+ba_file = hf_hub_download(
+    repo_id="YYJMAY/modality-ood",
+    filename="ba_s.csv",
+    repo_type="dataset"
+)
+ba_df = pd.read_csv(ba_file)
+
+# Download EL dataset
+el_file = hf_hub_download(
+    repo_id="YYJMAY/modality-ood",
+    filename="el_s.csv",
+    repo_type="dataset"
+)
+el_df = pd.read_csv(el_file)
+```
+
+### Use with SPRINT Framework
+
+```python
+from sprint.core.dataset_manager import DatasetManager
+
+manager = DatasetManager()
+config = {
+    'hf_repo': 'YYJMAY/modality-ood',
+    'files': ['ba_s.csv', 'el_s.csv'],
+    'ba': 'ba_s.csv',
+    'el': 'el_s.csv'
+}
+
+files = manager.get_dataset('modality_ood', config)
+ba_file = files['ba']
+el_file = files['el']
+```
+
+### Example: Cross-Modality Evaluation
+
+```python
+import pandas as pd
+from your_model import YourModel
+
+# Load data
+ba_df = pd.read_csv(ba_file)
+el_df = pd.read_csv(el_file)
+
+# Scenario 1: Train on BA, test on EL
+model = YourModel()
+model.train(ba_df)
+el_predictions = model.predict(el_df)
+
+# Scenario 2: Train on EL, test on BA
+model = YourModel()
+model.train(el_df)
+ba_predictions = model.predict(ba_df)
+
+# Evaluate cross-modality generalization
+```
+
+## Experimental Design
+
+### Recommended Evaluation Scenarios
+
+1. **BA → EL Generalization**
+   - Train on BA (continuous labels)
+   - Test on EL (binary labels)
+   - Measures if affinity-based models predict presentation
+
+2. **EL → BA Generalization**
+   - Train on EL (binary labels)
+   - Test on BA (continuous labels)
+   - Measures if presentation-based models predict affinity
+
+3. **Mixed Training**
+   - Train on both BA and EL
+   - Test separately on each
+   - Measures multi-task learning benefits
+
+4. **Modality-Specific Training**
+   - Train and test on same modality
+   - Baseline for comparison
+
+### Metrics Considerations
+
+- **For BA**: Use regression metrics (MSE, MAE, Pearson correlation)
+- **For EL**: Use classification metrics (AUC, F1, precision, recall)
+- **Cross-modal**: May need to binarize BA predictions or convert EL to scores
+
+## Construction Method
+
+Both datasets were constructed to ensure:
+
+1. **Single Allele Format**: Each sample has exactly one HLA allele
+2. **Quality Control**: 
+   - No missing values in required columns
+   - No duplicate peptide-HLA-label combinations
+   - Peptide lengths filtered to 8-15 amino acids
+3. **Standardized HLA Format**: HLA-A02:01 format (with hyphen prefix)
+4. **Representative Coverage**: 130+ HLA alleles across major supertypes
+5. **Balanced Lengths**: Both datasets include diverse peptide lengths
+
+## Citation
+
+If you use this dataset, please cite:
+
+```bibtex
+@dataset{modality_ood_2024,
+  title={Modality OOD Dataset for Peptide-MHC Binding Prediction},
+  author={SPRINT Framework Contributors},
+  year={2024},
+  url={https://huggingface.co/datasets/YYJMAY/modality-ood}
+}
+```
+
+## Related Datasets
+
+- **Allelic OOD**: Tests generalization to rare HLA alleles
+- **Temporal OOD**: Tests generalization to new data over time
+
+## Notes
+
+- **No CDR3 sequences**: These datasets are for PM (Peptide-MHC) tasks only, not PMT (Peptide-MHC-TCR)
+- **Label semantics differ**: BA is continuous affinity, EL is binary presentation
+- **Experimental platforms differ**: BA from in vitro assays, EL from mass spectrometry
+- **Biological processes differ**: BA measures binding only, EL captures full pathway
+
+## License
+
+MIT License
+
+## Contact
+
+For questions or issues, please open an issue on the dataset repository.
+
+---
+
+**Keywords**: peptide-MHC binding, immunology, binding affinity, eluted ligand, modality shift, out-of-distribution, generalization, cross-modal learning