{"access":"open","annotations":"Quantitative IC50 values from competitive binding assays with a radiolabelled reference peptide, or fluorescence-based equivalents.","article":"/blog/modele-prezentacji-antygenu","cancer_slugs":["pan-cancer"],"details":"","doi":null,"formats":["CSV"],"hf":null,"huggingface":null,"kind":"benchmark","license":"public","modalities":["protein-sequence"],"models":[],"name":"BD2013 \u2014 MHC binding affinity benchmark","page":"/ai-oncology/datasets/bd2013","provider":"IEDB / Kim et al.","size":{"items":176161,"notes_en":"114 alleles across six species \u2014 the historical training core for binding predictors, and a reminder of how small the measured world is","notes_pl":"114 alleli w sze\u015bciu gatunkach \u2014 historyczny rdze\u0144 treningowy modeli wi\u0105zania i przypomnienie, jak ma\u0142y jest \u015bwiat zmierzony","unit":"affinity measurements"},"slug":"bd2013","sources":[{"label":"Kim Y et al. Dataset size and composition impact the reliability of performance benchmarks. BMC Bioinformatics 2014","url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC4111843/"}],"summary":"The reference affinity dataset behind fifteen years of binding predictors; its size and composition are why dataset composition, not architecture, drives reported performance.","tasks":["peptide-mhc-binding"],"url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC4111843/","verified_at":"2026-09-05T22:25:59.496418"}