{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003774915,0.002499345,0.002145921,0.002706014,0.001501793,0.003134249,0.002891533,0.001712106,0.08009344],"category_scores_gemma":[0.007246582,0.001911963,0.001504205,0.00186899,0.0007140468,0.002608554,0.002894268,0.004954477,0.02896346],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007179477,"about_ca_system_score_gemma":0.001274674,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008387016,"about_ca_topic_score_gemma":0.001207182,"domain_scores_codex":[0.9982145,0.0002848368,0.0001725974,0.00048949,0.0005954571,0.0002432647],"domain_scores_gemma":[0.9975751,0.001132837,0.0002021564,0.0004925709,0.0003630445,0.0002342345],"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":[0.003851319,0.0003358565,0.004168769,0.005098312,0.001053775,0.002456828,0.001592219,0.003093439,0.4780229,0.01457224,0.3814064,0.1043479],"study_design_scores_gemma":[0.0005777909,0.0001696672,0.005209624,0.0006272459,0.000135444,0.001764481,0.0002331665,0.03035369,0.5133795,0.009407541,0.4377098,0.0004321682],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02999265,0.002496179,0.5991678,0.00153841,0.0009247249,0.001005683,0.07639352,0.2784343,0.01004674],"genre_scores_gemma":[0.06762264,0.002657723,0.7123977,0.001608216,0.0002405542,0.007861712,0.09647915,0.1016492,0.009483153],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.08009344,"threshold_uncertainty_score":0.2679393,"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."}}