Add comprehensive README with dataset description

README.md

@@ -1,176 +1,183 @@
----
-language:
-- en
-tags:
-- biology
-- immunology
-- tcr
-- peptide
-- t-cell-receptor
-- binding-prediction
-task_categories:
-- text-classification
-size_categories:
-- 10K<n<100K
----
-
-# PT Interaction Dataset
-
-## Dataset Description
-
-The **PT (Peptide-TCR) interaction dataset** is designed for training and evaluating T-Cell Receptor (TCR) binding prediction models with full TCR sequence information. This dataset contains paired peptide sequences and complete TCR alpha/beta chain sequences (including all 6 CDR regions: A1-A3, B1-B3), along with binary binding labels.
-
-### Key Features
-
-- **Full TCR Information**: Contains all 6 CDR regions (A1, A2, A3, B1, B2, B3) for both alpha and beta chains
-- **Binary Labels**: Binding labels (0=non-binder, 1=binder)
-- **HLA Allele Information**: MHC allele context for each peptide-TCR pair
-- **Peptide Length Range**: 8-12 amino acids
-- **CDR3β Length Range**: 5-23 amino acids
-- **Training Set**: 43,378 samples (13.62% positive, 86.38% negative)
-- **Test Set**: 2,956 samples (13.97% positive, 86.03% negative)
-
-### Dataset Statistics
-
-| Split | Samples | Positives | Negatives | Unique TCRs | Unique HLAs |
-|-------|---------|-----------|-----------|-------------|-------------|
-| Train | 43,378 | 5,906 (13.62%) | 37,472 (86.38%) | 10,414 | 10 |
-| ID Test | 2,956 | 413 (13.97%) | 2,543 (86.03%) | 2,511 | 10 |
-
-### Data Format
-
-Each row contains the following columns:
-
-- `peptide`: Amino acid sequence of the peptide (8-12 aa)
-- `A1`, `A2`, `A3`: CDR1α, CDR2α, CDR3α sequences
-- `B1`, `B2`, `B3`: CDR1β, CDR2β, CDR3β sequences
-- `binder`: Binary binding label (0=non-binder, 1=binder)
-- `allele`: HLA allele (e.g., A*02:01, B*07:02)
-
-### Example Data
-
-```python
-{
-    "peptide": "KLGGALQAK",
-    "A1": "SSVPPY",
-    "A2": "YTSAATLV",
-    "A3": "AVKWSSNYKLT",
-    "B1": "SQVTM",
-    "B2": "ANQGSEA",
-    "B3": "SVGSGDHGEQF",
-    "binder": 0,
-    "allele": "A*03:01"
-}
-```
-
-## Dataset Construction
-
-
-### Quality Control
-
-1. **TCR Leakage Prevention**: Train and test splits are carefully constructed to ensure no TCR overlap based on CDR3β sequences
-2. **Duplicate Removal**: All duplicate (peptide, B3, binder) combinations are removed
-3. **Length Filtering**: Only peptides of length 8-12 amino acids are included
-4. **HLA Standardization**: All HLA alleles follow the format "A*02:01" (without "HLA-" prefix)
-5. **Data Validation**: All sequences are validated for amino acid composition
-
-### Split Strategy
-
-- **ID Test**: Random split preserving the same peptide/HLA/TCR distribution as training
-- **No TCR Leakage**: Train and test sets are strictly disjoint based on CDR3β sequences
-
-## Usage
-
-### Loading the Dataset
-
-```python
-from datasets import load_dataset
-
-# Load the entire dataset
-dataset = load_dataset("YYJMAY/pt-interaction")
-
-# Access splits
-train_data = dataset['train']
-test_data = dataset['test']
-
-# Convert to pandas DataFrame
-import pandas as pd
-train_df = pd.DataFrame(train_data)
-test_df = pd.DataFrame(test_data)
-```
-
-### Training Example
-
-```python
-from datasets import load_dataset
-import pandas as pd
-
-# Load training data
-dataset = load_dataset("YYJMAY/pt-interaction", split="train")
-df = pd.DataFrame(dataset)
-
-# Prepare features
-X = df[['peptide', 'A1', 'A2', 'A3', 'B1', 'B2', 'B3', 'allele']]
-y = df['binder']
-
-# Train your model
-# model.fit(X, y)
-```
-
-### Evaluation Example
-
-```python
-from datasets import load_dataset
-import pandas as pd
-from sklearn.metrics import roc_auc_score, accuracy_score
-
-# Load test data
-dataset = load_dataset("YYJMAY/pt-interaction", split="test")
-df = pd.DataFrame(dataset)
-
-# Make predictions
-X_test = df[['peptide', 'A1', 'A2', 'A3', 'B1', 'B2', 'B3', 'allele']]
-y_test = df['binder']
-
-# predictions = model.predict(X_test)
-# print(f"AUC: {roc_auc_score(y_test, predictions):.4f}")
-# print(f"Accuracy: {accuracy_score(y_test, predictions > 0.5):.4f}")
-```
-
-## Citation
-
-If you use this dataset in your research, please cite:
-
-```bibtex
-@misc{pt_interaction_dataset,
-  title={PT Interaction Dataset: Peptide-TCR Binding Prediction},
-  author={SPRINT Benchmark Contributors},
-  year={2025},
-  howpublished={\url{https://huggingface.co/datasets/YYJMAY/pt-interaction}}
-}
-```
-
-## Related Datasets
-
-- **PM Dataset**: Peptide-MHC binding (no TCR information)
-- **PMT Dataset**: Peptide-MHC-TCR with CDR3β only
-- **Allelic OOD**: Out-of-distribution test for rare HLA alleles
-- **Temporal OOD**: Out-of-distribution test for COVID-19 era data
-- **Modality OOD**: Cross-modality generalization (BA vs EL)
-
-## License
-
-This dataset is released under the MIT License. The original data sources may have their own licenses.
-
-## Contact
-
-For questions or issues, please open an issue on the [SPRINT GitHub repository](https://github.com/Computational-Machine-Intelligence/SPRINT).
-
-## Dataset Card Authors
-
-SPRINT Benchmark Team
-
-## Dataset Version
-
-- **Version**: 1.0
-- **Last Updated**: 2025-01-19
+---
+language:
+- en
+tags:
+- biology
+- immunology
+- tcr
+- peptide
+- t-cell-receptor
+- binding-prediction
+task_categories:
+- text-classification
+size_categories:
+- 10K<n<100K
+---
+
+# PT Interaction Dataset
+
+## Dataset Description
+
+The **PT (Peptide-TCR) interaction dataset** is designed for training and evaluating T-Cell Receptor (TCR) binding prediction models with full TCR sequence information. This dataset contains paired peptide sequences and complete TCR alpha/beta chain sequences (including all 6 CDR regions: A1-A3, B1-B3), along with binary binding labels.
+
+### Key Features
+
+- **Full TCR Information**: Contains all 6 CDR regions (A1, A2, A3, B1, B2, B3) for both alpha and beta chains
+- **Binary Labels**: Binding labels (0=non-binder, 1=binder)
+- **HLA Allele Information**: MHC allele context for each peptide-TCR pair
+- **Peptide Length Range**: 8-12 amino acids
+- **CDR3β Length Range**: 5-23 amino acids
+- **Training Set**: 43,378 samples (13.62% positive, 86.38% negative)
+- **Test Set**: 2,956 samples (13.97% positive, 86.03% negative)
+
+### Dataset Statistics
+
+| Split | Samples | Positives | Negatives | Unique TCRs | Unique HLAs |
+|-------|---------|-----------|-----------|-------------|-------------|
+| Train | 43,378 | 5,906 (13.62%) | 37,472 (86.38%) | 10,414 | 10 |
+| ID Test | 2,956 | 413 (13.97%) | 2,543 (86.03%) | 2,511 | 10 |
+
+### Data Format
+
+Each row contains the following columns:
+
+- `peptide`: Amino acid sequence of the peptide (8-12 aa)
+- `A1`, `A2`, `A3`: CDR1α, CDR2α, CDR3α sequences
+- `B1`, `B2`, `B3`: CDR1β, CDR2β, CDR3β sequences
+- `binder`: Binary binding label (0=non-binder, 1=binder)
+- `allele`: HLA allele (e.g., A*02:01, B*07:02)
+
+### Example Data
+
+```python
+{
+    "peptide": "KLGGALQAK",
+    "A1": "SSVPPY",
+    "A2": "YTSAATLV",
+    "A3": "AVKWSSNYKLT",
+    "B1": "SQVTM",
+    "B2": "ANQGSEA",
+    "B3": "SVGSGDHGEQF",
+    "binder": 0,
+    "allele": "A*03:01"
+}
+```
+
+## Dataset Construction
+
+### Data Sources
+
+The PT dataset is curated from multiple publicly available TCR-peptide binding databases and experimental studies, including:
+- VDJdb: A curated database of T-cell receptor sequences
+- McPAS-TCR: Manually curated catalog of pathology-associated TCR sequences
+- IEDB: Immune Epitope Database
+- Published experimental validation studies
+
+### Quality Control
+
+1. **TCR Leakage Prevention**: Train and test splits are carefully constructed to ensure no TCR overlap based on CDR3β sequences
+2. **Duplicate Removal**: All duplicate (peptide, B3, binder) combinations are removed
+3. **Length Filtering**: Only peptides of length 8-12 amino acids are included
+4. **HLA Standardization**: All HLA alleles follow the format "A*02:01" (without "HLA-" prefix)
+5. **Data Validation**: All sequences are validated for amino acid composition
+
+### Split Strategy
+
+- **ID Test**: Random split preserving the same peptide/HLA/TCR distribution as training
+- **No TCR Leakage**: Train and test sets are strictly disjoint based on CDR3β sequences
+
+## Usage
+
+### Loading the Dataset
+
+```python
+from datasets import load_dataset
+
+# Load the entire dataset
+dataset = load_dataset("YYJMAY/pt-interaction")
+
+# Access splits
+train_data = dataset['train']
+test_data = dataset['test']
+
+# Convert to pandas DataFrame
+import pandas as pd
+train_df = pd.DataFrame(train_data)
+test_df = pd.DataFrame(test_data)
+```
+
+### Training Example
+
+```python
+from datasets import load_dataset
+import pandas as pd
+
+# Load training data
+dataset = load_dataset("YYJMAY/pt-interaction", split="train")
+df = pd.DataFrame(dataset)
+
+# Prepare features
+X = df[['peptide', 'A1', 'A2', 'A3', 'B1', 'B2', 'B3', 'allele']]
+y = df['binder']
+
+# Train your model
+# model.fit(X, y)
+```
+
+### Evaluation Example
+
+```python
+from datasets import load_dataset
+import pandas as pd
+from sklearn.metrics import roc_auc_score, accuracy_score
+
+# Load test data
+dataset = load_dataset("YYJMAY/pt-interaction", data_files="id_test.csv")
+df = pd.DataFrame(dataset['train'])  # HF loads single files as 'train' split
+
+# Make predictions
+X_test = df[['peptide', 'A1', 'A2', 'A3', 'B1', 'B2', 'B3', 'allele']]
+y_test = df['binder']
+
+# predictions = model.predict(X_test)
+# print(f"AUC: {roc_auc_score(y_test, predictions):.4f}")
+# print(f"Accuracy: {accuracy_score(y_test, predictions > 0.5):.4f}")
+```
+
+## Citation
+
+If you use this dataset in your research, please cite:
+
+```bibtex
+@misc{pt_interaction_dataset,
+  title={PT Interaction Dataset: Peptide-TCR Binding Prediction},
+  author={SPRINT Benchmark Contributors},
+  year={2025},
+  howpublished={\url{https://huggingface.co/datasets/YYJMAY/pt-interaction}}
+}
+```
+
+## Related Datasets
+
+- **PM Dataset**: Peptide-MHC binding (no TCR information)
+- **PMT Dataset**: Peptide-MHC-TCR with CDR3β only
+- **Allelic OOD**: Out-of-distribution test for rare HLA alleles
+- **Temporal OOD**: Out-of-distribution test for COVID-19 era data
+- **Modality OOD**: Cross-modality generalization (BA vs EL)
+
+## License
+
+This dataset is released under the MIT License. The original data sources may have their own licenses.
+
+## Contact
+
+For questions or issues, please open an issue on the [SPRINT GitHub repository](https://github.com/Computational-Machine-Intelligence/SPRINT).
+
+## Dataset Card Authors
+
+SPRINT Benchmark Team
+
+## Dataset Version
+
+- **Version**: 1.0
+- **Last Updated**: 2025-01-19