{"id":"W2884325264","doi":"10.1093/bioinformatics/bty621","title":"MAVIS: merging, annotation, validation, and illustration of structural variants","year":2018,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Genomics and Rare Diseases","field":"Biochemistry, Genetics and Molecular Biology","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University; University of British Columbia; Canada's Michael Smith Genome Sciences Centre","funders":"Genome British Columbia; BC Cancer Foundation; Genome Canada","keywords":"Annotation; Context (archaeology); Computer science; Key (lock); Computational biology; Process (computing); Genome; Data mining; Data science; Artificial intelligence; Biology; Genetics; Programming language; Gene","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":[],"consensus_categories":[],"category_scores_codex":[0.00005784296,0.00006383919,0.00005736061,0.00002760867,0.00006284042,0.00002007151,0.0000547741,0.00004921733,0.00001461049],"category_scores_gemma":[0.00004188752,0.0000564754,0.0000191667,0.00004281324,0.00008066338,0.000009586679,0.0000320813,0.00001382897,0.000003699132],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000002743036,"about_ca_system_score_gemma":0.00004059315,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005257638,"about_ca_topic_score_gemma":0.000003788527,"domain_scores_codex":[0.9995902,0.000006765043,0.0001963706,0.00006582721,0.0000634158,0.00007746476],"domain_scores_gemma":[0.9995668,0.000002753127,0.0001108201,0.0001226468,0.00016016,0.00003680794],"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":[0.0005042334,0.0001879632,0.06402973,0.001020038,0.0006568187,0.000003355963,0.01101509,0.0005789885,0.8046904,0.0143953,0.02679847,0.07611962],"study_design_scores_gemma":[0.002975032,0.001783095,0.2842289,0.00008894657,0.0001953556,0.0001442056,0.003472754,0.03313277,0.6459956,0.002617431,0.02424549,0.001120361],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9958525,0.0000893316,0.003008371,0.00002669238,0.0001026301,0.00008242398,0.00004339361,0.000004813786,0.0007898238],"genre_scores_gemma":[0.994396,0.00003356352,0.005114117,0.00006716562,0.0001041317,0.000001591927,0.0002031625,0.000004792124,0.0000755336],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2201992,"threshold_uncertainty_score":0.2303,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009028182947586842,"score_gpt":0.2415304510133573,"score_spread":0.2325022680657705,"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."}}