{"id":"W4403003980","doi":"10.3389/fimmu.2024.1463931","title":"Integrating machine learning to advance epitope mapping","year":2024,"lang":"en","type":"review","venue":"Frontiers in Immunology","topic":"vaccines and immunoinformatics approaches","field":"Biochemistry, Genetics and Molecular Biology","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"National Institute of Allergy and Infectious Diseases; Canadian Institutes of Health Research; National Institutes of Health","keywords":"Epitope; Computer science; Epitope mapping; Computational biology; Artificial intelligence; Machine learning; Linear epitope; Feature (linguistics); Antigen; Biology; Immunology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001430757,0.0009532443,0.001386245,0.001835844,0.0001610349,0.001213399,0.001012015,0.001278088,0.002618002],"category_scores_gemma":[0.00177549,0.0003266615,0.00079159,0.001613197,0.0006312131,0.001633175,0.00071532,0.003260932,0.001736743],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009575315,"about_ca_system_score_gemma":0.001046551,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008566186,"about_ca_topic_score_gemma":0.001094479,"domain_scores_codex":[0.9996904,0.00009066701,0.00002558134,0.0000583528,0.0001078567,0.00002707169],"domain_scores_gemma":[0.9990733,0.0006337635,0.0000574784,0.00003568466,0.000159716,0.00003999908],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00006044242,0.0001034403,0.0002751644,0.01130907,0.0002135713,0.0001393054,0.00005151404,0.004462127,0.002577635,0.02294931,0.0183099,0.9395486],"study_design_scores_gemma":[0.00004433992,0.0002348899,0.000949948,0.004496376,0.0002237946,0.0009062669,0.00004948911,0.004184398,0.003272853,0.0340024,0.9515635,0.00007184612],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0004291707,0.9868552,0.007602356,0.00131344,0.0005385198,0.00002274432,0.00005160383,0.00006235929,0.00312454],"genre_scores_gemma":[0.004751355,0.9866831,0.005992707,0.0007277144,0.0005063285,0.00003037845,0.0001160235,0.00001355433,0.001178938],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.002618002,"threshold_uncertainty_score":0.008758128,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01468516456710252,"score_gpt":0.2688121973310703,"score_spread":0.2541270327639678,"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."}}