{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003281631,0.0005115547,0.001236427,0.0004829659,0.0000797043,0.00005922033,0.0006160944,0.0005456049,0.000008940177],"category_scores_gemma":[0.0002503144,0.0004269175,0.0003353271,0.0004718899,0.00004845112,0.000007256922,0.0006377501,0.0009906767,0.00008643667],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001060669,"about_ca_system_score_gemma":0.0001509197,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001917123,"about_ca_topic_score_gemma":0.00001109278,"domain_scores_codex":[0.9979005,0.0001550515,0.0008238415,0.0005414128,0.00007622406,0.0005029019],"domain_scores_gemma":[0.9991554,0.00001491927,0.000234608,0.0005146278,0.00003377423,0.00004666327],"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":[0.00001399554,0.00001677012,0.00001955831,0.002592471,0.0003574797,0.000007914537,0.0002076705,0.00001366892,0.0001319306,0.0000632004,0.003674468,0.9929008],"study_design_scores_gemma":[0.0001346122,0.0001647865,0.000001826262,0.003092503,0.00009173633,0.0000694656,0.0004627188,0.0001572718,0.00002723444,0.00004209774,0.9953163,0.0004393957],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.00005917076,0.9908685,0.005621601,0.00003410865,0.002027759,0.0005321186,0.0000217398,0.00003261059,0.0008023619],"genre_scores_gemma":[0.00001763753,0.9733948,0.02355574,0.00005130211,0.0001748092,0.0001860737,0.0005659331,0.000091731,0.001961943],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9924615,"threshold_uncertainty_score":0.9998183,"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."}}