{"id":"W3162925121","doi":"10.1101/2020.10.06.323162","title":"CancerVar: an Artificial Intelligence empowered platform for clinical interpretation of somatic mutations in cancer","year":2020,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Genomics and Rare Diseases","field":"Biochemistry, Genetics and Molecular Biology","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre; University of Toronto; University Health Network","funders":"U.S. National Library of Medicine; National Institute of General Medical Sciences; National Human Genome Research Institute; National Institutes of Health","keywords":"Clinical significance; Artificial intelligence; Machine learning; Computer science; Cancer; Somatic cell; Interpretation (philosophy); Computational biology; Bioinformatics; Medicine; Biology; Genetics; Gene; Pathology","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":[0.002858584,0.001444995,0.0007710728,0.004737313,0.0003831893,0.001791765,0.002112115,0.001430067,0.00626185],"category_scores_gemma":[0.01291583,0.0005943451,0.001299377,0.001797576,0.0004005925,0.001344823,0.003046475,0.001581907,0.002790758],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008071089,"about_ca_system_score_gemma":0.001535831,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003513827,"about_ca_topic_score_gemma":0.005791707,"domain_scores_codex":[0.9983482,0.0004518883,0.000193796,0.0004688669,0.0004715826,0.00006574688],"domain_scores_gemma":[0.995804,0.002532223,0.0004499182,0.0005279616,0.0004602482,0.0002257229],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002339078,0.0006120447,0.04254333,0.0033324,0.001523673,0.003178341,0.0007559682,0.1071536,0.01749137,0.01194466,0.2133382,0.5957873],"study_design_scores_gemma":[0.0003933659,0.0002684651,0.008715572,0.0004302094,0.0002883016,0.00124667,0.0001303234,0.8541083,0.01680749,0.03801654,0.07940659,0.0001880466],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"software","genre_scores_codex":[0.05985937,0.007708365,0.5040812,0.004877242,0.0007127603,0.001428323,0.06024043,0.3521491,0.008943219],"genre_scores_gemma":[0.3672624,0.002531402,0.5433198,0.003792274,0.0003362392,0.001168244,0.07231619,0.004940137,0.004333255],"genre_candidate":"software","genre_consensus":null,"teacher_disagreement_score":0.00626185,"threshold_uncertainty_score":0.02094799,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05480175609288389,"score_gpt":0.3376834711003492,"score_spread":0.2828817150074653,"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."}}