{"id":"W2741742659","doi":"10.1093/nar/gkx664","title":"The SysteMHC Atlas project","year":2017,"lang":"en","type":"article","venue":"Nucleic Acids Research","topic":"vaccines and immunoinformatics approaches","field":"Biochemistry, Genetics and Molecular Biology","cited_by":144,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Institute for Research in Immunology and Cancer","funders":"National Institute of Allergy and Infectious Diseases; National Institute of General Medical Sciences; Norges Forskningsråd; Nederlandse Organisatie voor Wetenschappelijk Onderzoek; Wellcome Trust; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; National Cancer Institute; National Institutes of Health; National Science Foundation","keywords":"Biology; Major histocompatibility complex; Computational biology; Pipeline (software); Context (archaeology); Atlas (anatomy); Computer science; Data science; Genetics; Antigen","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":[0.004625455,0.002322523,0.001637843,0.007480036,0.002113099,0.007997829,0.006375717,0.002474419,0.136632],"category_scores_gemma":[0.01020377,0.001462194,0.002244852,0.007806062,0.0009086961,0.005842451,0.007479201,0.002662914,0.1564659],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002560087,"about_ca_system_score_gemma":0.005265883,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008305651,"about_ca_topic_score_gemma":0.007422061,"domain_scores_codex":[0.9962555,0.0007817852,0.0003070846,0.001061799,0.00114902,0.0004447215],"domain_scores_gemma":[0.9946154,0.001185098,0.0004375777,0.001715532,0.001462833,0.0005834795],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002443266,0.00002246582,0.001047282,0.0009247117,0.0001071455,0.0001331633,0.000210361,0.001150428,0.001296401,0.01588071,0.9489548,0.03002816],"study_design_scores_gemma":[0.00005100854,0.00001389166,0.0007258654,0.0001780098,0.00004559982,0.0001525091,0.0000584484,0.002249204,0.001402233,0.01401421,0.9810678,0.00004119314],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.002700218,0.003630436,0.07058784,0.002743162,0.001188236,0.0003525933,0.6935837,0.1696573,0.05555653],"genre_scores_gemma":[0.009015548,0.001917833,0.05438437,0.001188088,0.0003597595,0.0007594466,0.8855033,0.03327851,0.01359315],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.136632,"threshold_uncertainty_score":0.4570795,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06768324745799226,"score_gpt":0.3618527550203449,"score_spread":0.2941695075623527,"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."}}