| # Dataset Summary | |
| - The EpicNet dataset is a curated integration of peptide and protein epitope records extracted from the Immune Epitope Database (IEDB) and related immunoinformatics resources. It standardizes linear peptide epitope representations, cross-links source molecules, organisms, and taxonomy references, and unifies metadata under consistent ontological mappings. | |
| - It supports bioinformatics, machine learning on epitope prediction, and immunotherapy research by providing aligned, structured representations of epitopes, their parent proteins, and associated immune contexts. | |
| # Supported Tasks and Use Cases | |
| - Epitope–Protein Mapping — link and analyze how antigenic peptides relate to their source proteins and organisms. | |
| - Sequence-Based Epitope Prediction — train models to predict immunogenic peptide segments. | |
| - Antigen Similarity & Cross-reactivity Studies — compare epitopes across species for vaccine development. | |
| - Knowledge Graph Integration — generate RDF/OWL graphs for ontology-powered immunoinformatics. | |
| - Embedding Generation and Representation Learning using transformers or peptide encoders (e.g., ESM, ProtBERT). | |
| # Dataset Structure | |
| ## Record Count | |
| - 2,308,224 rows representing peptide–protein associations | |
| - Average peptide length: 8–35 amino acids | |
| - Approximate dataset size: 85.8 MB (raw TSV) / 156 MB (Parquet) | |
| # Data Sources and Provenance | |
| - IEDB (Immune Epitope Database) — canonical epitope and antigen relationships | |
| - UniProt / NCBI Protein — sequence-level references | |
| - NCBI Taxonomy — standardized organism identifiers | |
| - Each entry maps identifiers to IEDB IRIs, providing internationally traceable cross‑links. | |
| # Languages and Data Format | |
| - Sequence representation: Amino acid single-letter codes (A–Y) | |
| - Metadata fields: English-language labels and ontology-class terms | |
| - Format: TSV / Parquet (CSV-compatible with structured metadata) | |
| # Intended Uses | |
| - Computational immunology pipelines (e.g., T-cell / B-cell epitope classifier fine-tuning) | |
| - Graph-based representation of immune networks | |
| - Comparative peptide analytics across pathogens and hosts | |
| - Foundation datasets for large-scale protein–epitope pretraining | |
| # Limitations and Ethical Considerations | |
| - Dataset reflects experimentally or computationally curated peptide sequences; not all records are experimentally verified. | |
| - Certain peptide segments appear across species due to homologs or predicted analogs. | |
| - Users must verify NCBI Taxonomy and UniProt IDs for clinical or diagnostic applications. | |
| - Dataset does not contain personally identifiable data or clinical metadata. | |
| # Citation | |
| - If you use this dataset, please cite: | |
| - Gokul Alex (2025). EpicNet – International Epitope Database Peptide Network. Hugging Face Datasets. | |
| - Available at: https://huggingface.co/datasets/gokulalex/EpicNet | |
| # License | |
| - Creative Commons Attribution 4.0 International (CC BY‑4.0) | |
| # Dataset Creation and Maintenance | |
| - Author: Gokul Alex | |
| - Release Date: October 2025 | |
| - Version: v1.0 | |
| - Contact: huggingface.co/gokulalex | |
| # Example Entry | |
| json | |
| { | |
| "IEDB IRI": "http://www.iedb.org/epitope/1", | |
| "Object Type": "Linear peptide", | |
| "Name": "AA + MCM(A1,A2)", | |
| "Modified Residue(s)": "A1,A2", | |
| "Modifications": "Main chain modification", | |
| "Starting Position": 200, | |
| "Ending Position": 201, | |
| "Source Molecule": "Streptokinase", | |
| "Source Molecule IRI": "https://uniprot.org/uniprot/P10520", | |
| "Source Organism": "Streptococcus pyogenes", | |
| "Species": "Streptococcus pyogenes serotype M3 D58" | |
| } | |
| # Acknowledgments | |
| - Data derived from the IEDB Consortium, NCBI, and UniProt KB. | |
| - Compilation, integration, and ontology alignment under the EpicNet Data Initiative. |