{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002549421,0.000141479,0.000225565,0.00006684824,0.0001530863,0.00004882839,0.0001574762,0.0001233193,0.000004572933],"category_scores_gemma":[0.0001335684,0.0001393907,0.00003759671,0.0001186175,0.00002386968,0.000008057284,0.0002796109,0.0001815522,0.0000120214],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001912873,"about_ca_system_score_gemma":0.00004581844,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002787279,"about_ca_topic_score_gemma":0.00001022146,"domain_scores_codex":[0.9991445,0.00002633367,0.0002621931,0.0002403504,0.0001082367,0.0002183789],"domain_scores_gemma":[0.9995176,0.0000301564,0.00007936923,0.0002377068,0.00005407831,0.00008111128],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001236712,0.0001831629,0.4375938,0.0001926174,0.0004244977,3.381438e-7,0.0001805986,0.00004648697,0.5552803,0.0001184643,0.0001130603,0.005742953],"study_design_scores_gemma":[0.003097705,0.001982153,0.3132958,0.0004450273,0.0005615698,0.000004654278,0.0008739867,0.0147027,0.6543183,0.00007702071,0.00977986,0.0008611901],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9894218,0.0002004674,0.006805325,0.0002222113,0.00001185123,0.000334753,0.00001893029,0.00002239433,0.002962289],"genre_scores_gemma":[0.9953848,0.0001103055,0.003209784,0.0001187709,0.00009696771,0.00005992554,0.0002921088,0.00001470087,0.0007126236],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.124298,"threshold_uncertainty_score":0.5684187,"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."}}