{"id":"W4411727996","doi":"10.1371/journal.pone.0314833","title":"Machine learning application to predict binding affinity between peptide containing non-canonical amino acids and HLA-A0201","year":2025,"lang":"en","type":"article","venue":"PLoS ONE","topic":"vaccines and immunoinformatics approaches","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"U.S. National Library of Medicine; Alexander von Humboldt-Stiftung; National Institute of Allergy and Infectious Diseases; National Institute on Drug Abuse; National Heart, Lung, and Blood Institute; National Cancer Institute; National Institutes of Health; Bundesministerium für Bildung und Forschung; National Institute on Aging; Deutsche Forschungsgemeinschaft; German Network for Bioinformatics Infrastructure; Deutscher Akademischer Austauschdienst","keywords":"Immunogenicity; Major histocompatibility complex; Human leukocyte antigen; Epitope; Computational biology; MHC class I; Biology; Peptide; Antigen; Amino acid; Biochemistry; Chemistry; Genetics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001062015,0.0006659482,0.000590299,0.0006751833,0.0002614647,0.0005242902,0.0005446055,0.0008645724,0.001124209],"category_scores_gemma":[0.001819616,0.0002323224,0.0006202916,0.0005066227,0.0001938666,0.0002513142,0.0003477993,0.0006448074,0.0003772723],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004797779,"about_ca_system_score_gemma":0.0007447429,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005000324,"about_ca_topic_score_gemma":0.002894228,"domain_scores_codex":[0.9997141,0.0001064055,0.00002052482,0.00006573175,0.00005470974,0.0000385502],"domain_scores_gemma":[0.999121,0.0005903417,0.00007379839,0.00002931133,0.0001574666,0.00002798524],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001640389,0.0002981202,0.008569218,0.0000703356,0.0001111895,0.00009571189,0.00002550773,0.9019161,0.007406255,0.0004273697,0.0006694293,0.08024674],"study_design_scores_gemma":[0.000002864024,0.00002968078,0.0004912668,0.000002264994,0.000004692143,0.000007140397,0.000002185565,0.998439,0.0008544063,0.00009127575,0.0000730231,0.000002279744],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6999492,0.001516849,0.2912659,0.0005372895,0.0001234198,0.0001290849,0.0004029546,0.002151297,0.003924026],"genre_scores_gemma":[0.9548781,0.0001838481,0.04278229,0.00009259072,0.00002230887,0.00007689177,0.0003369002,0.00002248359,0.001604539],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005000324,"threshold_uncertainty_score":0.009942412,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02106791223172387,"score_gpt":0.237045655405543,"score_spread":0.2159777431738191,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}