{"id":"W4308957884","doi":"10.1093/nar/gkac979","title":"CIViCdb 2022: evolution of an open-access cancer variant interpretation knowledgebase","year":2022,"lang":"en","type":"article","venue":"Nucleic Acids Research","topic":"Cancer Genomics and Diagnostics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":46,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre; Ontario Institute for Cancer Research; University of Toronto; University Health Network; Toronto General Hospital; Hospital for Sick Children; York University; University of British Columbia; Sinai Health System; Canada's Michael Smith Genome Sciences Centre","funders":"Cancer Moonshot; National Center for Advancing Translational Sciences; National Human Genome Research Institute; Berlin Institute of Health; Institute of Clinical and Translational Sciences; Eli Lilly and Company; Bristol-Myers Squibb; Moonshot Research and Development Program; Charité – Universitätsmedizin Berlin; National Cancer Institute; National Institutes of Health; U.S. Department of Health and Human Services","keywords":"Biology; Interpretation (philosophy); Interoperability; Inference; Data curation; Computational biology; Germline; Cancer; Genetics; Data science; Gene; Computer science; World Wide Web; Artificial intelligence","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009666485,0.00008789549,0.0001244833,0.0001384102,0.0002830409,0.0001223694,0.001264684,0.0000598778,0.0008426447],"category_scores_gemma":[0.0002091686,0.00009706987,0.00003711374,0.0003934174,0.0001093459,0.00002077139,0.002698337,0.0002342586,0.000006648201],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002532365,"about_ca_system_score_gemma":0.0007497402,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001575356,"about_ca_topic_score_gemma":0.0007403882,"domain_scores_codex":[0.9985061,0.0002926804,0.000198641,0.0003874026,0.0003416852,0.0002735405],"domain_scores_gemma":[0.9989871,0.00003760133,0.00006927348,0.0004856024,0.0003188394,0.0001015287],"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":[0.00170661,0.000816645,0.00545403,0.00007790192,0.0001010162,0.00002153481,0.0005520253,0.00357337,0.9030503,0.003177435,0.02714211,0.05432703],"study_design_scores_gemma":[0.00648206,0.0104938,0.05902165,0.0001324215,0.00009891853,0.00007460506,0.003999962,0.06480893,0.2753905,0.009254945,0.5688197,0.001422551],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9919459,0.001673488,0.001025924,0.0002185803,0.0003316809,0.0004346676,0.000270637,0.000006400933,0.004092688],"genre_scores_gemma":[0.9981184,0.0002774639,0.0001936321,0.0000820735,0.0001834808,0.0002763406,0.0001542746,0.00002837139,0.0006859511],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6276598,"threshold_uncertainty_score":0.9226369,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04259425653971272,"score_gpt":0.4048559058308578,"score_spread":0.3622616492911451,"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."}}