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- name:
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---
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tags:
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- immunology
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- MHC
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- epitope
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- binding
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- peptide
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- HLA
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- biology
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- huggingscience
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- science
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pretty_name: IEDB MHC Class I Binding Dataset 2013
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configs:
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- config_name: default
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data_files:
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- split: train
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path: data/train-*
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- split: test
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path: data/test-*
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dataset_info:
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features:
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- name: Peptide
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dtype: string
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- name: Sequence
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dtype: string
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- name: HLA
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dtype: string
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- name: Length
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dtype: int64
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- name: Log_meas
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dtype: float64
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splits:
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- name: train
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num_bytes: 142687867
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num_examples: 125536
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- name: test
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num_bytes: 35661056
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num_examples: 31385
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download_size: 7716040
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dataset_size: 178348923
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size_categories:
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- 1M<n<10M
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---
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# IEDB MHC Class I Binding Dataset 2013
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## Dataset Description
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### Dataset Summary
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The IEDB 2013 dataset contains experimentally measured binding affinities between peptides and Major Histocompatibility Complex (MHC) class I molecules. This dataset is derived from the Immune Epitope Database (IEDB), which is a freely available resource containing an extensive collection of experimentally measured immune epitopes and tools for epitope analysis and prediction.
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The dataset includes binding measurements for various Human Leukocyte Antigen (HLA) alleles and peptides of different lengths, making it valuable for training machine learning models to predict MHC-peptide binding affinities - a crucial task in immunoinformatics, vaccine design, and personalized medicine.
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### Supported Tasks
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- **Regression**: The dataset can be used to train models for predicting MHC class I binding affinities, which consists of predicting the binding strength between a peptide and an MHC molecule. Success on this task is typically measured by achieving low mean squared error (MSE) or high Pearson correlation coefficients.
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## Getting Started
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### Quick Start
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```python
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from datasets import load_dataset
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# Load the dataset
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dataset = load_dataset("vladak/iedb-2013")
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# View the first few examples
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print(dataset["train"][:5])
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# Get dataset info
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print(f"Dataset size: {len(dataset['train'])}")
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print(f"Features: {dataset['train'].features}")
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```
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## Dataset Structure
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### Data Instances
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Each instance in the dataset represents a single binding measurement experiment between a peptide and an HLA allele:
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```json
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{
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"HLA": "HLA-A*02:01",
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"Peptide": "SLYNTVATL",
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"Log_meas": 2.45,
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"Length": 9,
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"Sequence": "SLYNTVATL"
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}
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```
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### Data Fields
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| Field Name | Type | Description |
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|------------|------|-------------|
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| `HLA` | `string` | HLA allele identifier using standard nomenclature (e.g., "HLA-A*02:01", "HLA-B*07:02") |
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| `Peptide` | `string` | Amino acid sequence of the peptide using single-letter codes |
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| `Log_meas` | `float64` | Logarithm of the measured binding affinity. Lower values indicate stronger binding |
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| `Length` | `int64` | Length of the peptide sequence in amino acids |
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| `Sequence` | `string` | Same as peptide field - amino acid sequence of the peptide |
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### Data Splits
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The dataset is divided in train split (126k rows) and test split (31.4k rows)-
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## Dataset Creation
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### Source Data
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The data originates from the Immune Epitope Database (IEDB), which systematically collects and curates immune epitope data from peer-reviewed scientific literature and direct data submissions from researchers. The specific data used in this dataset comes from the 2013 version of the IEDB binding data release.
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**Data Sources:**
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- Original IEDB binding data: http://tools.iedb.org/mhci/download/binding_data_2013.zip
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- Processed training set: https://raw.githubusercontent.com/s7776d/CapsNet-MHC/refs/heads/main/dataset/IEDB_dataset/training_set.txt
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- Processing code: https://github.com/vladimirkovacevic/nucl-protein/blob/main/notebooks/EDA_Datasets.ipynb
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