{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001667331,0.001875628,0.001156669,0.000911891,0.0005434777,0.00131916,0.001406052,0.0005193754,0.008163055],"category_scores_gemma":[0.002303149,0.0006664834,0.001273279,0.0008842848,0.0003473587,0.0009550711,0.0009671029,0.001812891,0.00567286],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000588124,"about_ca_system_score_gemma":0.001698523,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001192503,"about_ca_topic_score_gemma":0.001566485,"domain_scores_codex":[0.9994773,0.000100838,0.00003186014,0.0001134979,0.0002184212,0.00005813386],"domain_scores_gemma":[0.9993248,0.0002532542,0.00006910484,0.00009974318,0.0002084697,0.00004466563],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001563786,0.0005668947,0.007359774,0.00173936,0.0004564289,0.0008840303,0.0003354449,0.1146024,0.2336346,0.03605298,0.07039353,0.5324107],"study_design_scores_gemma":[0.0002705535,0.0004187243,0.002542498,0.0001586156,0.0002305068,0.0005652964,0.00008898022,0.6688922,0.1875049,0.03096869,0.1082336,0.0001255253],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02312356,0.0007324517,0.9384847,0.0004020668,0.00007850355,0.0002825364,0.004467262,0.02782323,0.00460574],"genre_scores_gemma":[0.113332,0.001407437,0.8598768,0.0003265103,0.00004418893,0.0006201275,0.01738872,0.003731436,0.003272786],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008163055,"threshold_uncertainty_score":0.02730817,"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."}}