{"id":"W2107414458","doi":"10.1093/bioinformatics/btv136","title":"EpIC: a rational pipeline for epitope immunogenicity characterization","year":2015,"lang":"en","type":"article","venue":"Bioinformatics","topic":"vaccines and immunoinformatics approaches","field":"Biochemistry, Genetics and Molecular Biology","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Immunogenicity; Epitope; Computational biology; Peptide vaccine; Computer science; Peptide; Epitope mapping; Pipeline (software); Biology; Virology; Antibody; Immunology; Biochemistry; Programming language","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":[0.0003196927,0.0001461812,0.0001390295,0.00004122503,0.00008508893,0.00006404938,0.000182224,0.0001184988,0.000008448387],"category_scores_gemma":[0.0001820228,0.0001288042,0.00008859655,0.00007337831,0.00002426959,0.00003089586,0.00009007158,0.00004518278,0.00003199973],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001938931,"about_ca_system_score_gemma":0.0001433992,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000182668,"about_ca_topic_score_gemma":0.000001565786,"domain_scores_codex":[0.9990969,0.00001112014,0.0004613834,0.00009243426,0.0001351857,0.0002029345],"domain_scores_gemma":[0.9991773,0.000007879896,0.0001938757,0.000277687,0.0002576034,0.00008564389],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.002946737,0.0009961342,0.005303638,0.001162808,0.0007741068,0.00000128735,0.006828832,0.002048368,0.5339099,0.01271985,0.1588945,0.2744139],"study_design_scores_gemma":[0.004334181,0.0006895554,0.001405486,0.00002725081,0.00006167849,0.00005892209,0.0009372819,0.317479,0.09114886,0.0003637365,0.5827698,0.0007242547],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.341974,0.0003056116,0.6524161,0.0004534789,0.0006132713,0.001010282,0.0002819379,0.00004760915,0.002897695],"genre_scores_gemma":[0.8731411,0.0003336982,0.1029433,0.001855517,0.001206922,0.0002259169,0.01697227,0.0000670806,0.003254129],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5494727,"threshold_uncertainty_score":0.5252483,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02918367636285201,"score_gpt":0.2518450514789635,"score_spread":0.2226613751161115,"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."}}