{"id":"W3095191596","doi":"10.1101/2020.11.02.360958","title":"Immunopeptidomics for Dummies: Detailed Experimental Protocols and Rapid, User-Friendly Visualization of MHC I and II Ligand Datasets with MhcVizPipe","year":2020,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"vaccines and immunoinformatics approaches","field":"Biochemistry, Genetics and Molecular Biology","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Institute for Research in Immunology and Cancer; Centre Hospitalier Universitaire Sainte-Justine","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds de Recherche du Québec - Santé; Institut de Valorisation des Données; Genome Canada","keywords":"Workflow; Computer science; Visualization; Major histocompatibility complex; Class (philosophy); MHC class I; Identification (biology); Software; Computational biology; Software engineering; Data mining; Data science; Programming language; Biology; Database; Artificial intelligence; Antigen","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":[0.0002453791,0.0004315189,0.0005008493,0.00007302298,0.0001698861,0.0001397545,0.0002836346,0.0003281554,0.000002656439],"category_scores_gemma":[0.00009050692,0.0003979632,0.00006713571,0.0001090913,0.0001260665,0.00002842226,0.0007996161,0.0001467369,4.528509e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002205233,"about_ca_system_score_gemma":0.0002148625,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007272924,"about_ca_topic_score_gemma":0.000001318131,"domain_scores_codex":[0.998426,0.00003761118,0.0005216539,0.0006008378,0.0001479843,0.0002658974],"domain_scores_gemma":[0.9984555,0.00001360916,0.000505102,0.0006938768,0.0002068825,0.0001250119],"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":[0.0005538297,0.0001247818,0.001269278,0.0009616109,0.0002405143,9.239716e-7,0.00004421407,0.000009614544,0.9961199,0.00024618,0.0004264895,0.00000265777],"study_design_scores_gemma":[0.00227965,0.001318565,0.00240605,0.0002834505,0.00009655887,9.94386e-8,0.00005008055,0.0006129372,0.9827135,6.723816e-7,0.009713933,0.0005245594],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9850805,0.001695723,0.004656876,0.00003743158,0.00006363828,0.007533255,0.000900274,0.00002987068,0.000002383062],"genre_scores_gemma":[0.9852261,0.0003762349,0.01074385,0.00007110315,0.0001051225,0.003340642,0.00004594875,0.00008843638,0.000002583096],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01340646,"threshold_uncertainty_score":0.9998472,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01458428286987188,"score_gpt":0.2491571103529956,"score_spread":0.2345728274831238,"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."}}