{"id":"W4366144498","doi":"10.1016/j.molmed.2023.03.007","title":"Cancer driver mutations: predictions and reality","year":2023,"lang":"en","type":"review","venue":"Trends in Molecular Medicine","topic":"Cancer Genomics and Diagnostics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":130,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institute of Cancer Research; Ontario Institute for Cancer Research; Kingston Health Sciences Centre; Queen's University","funders":"U.S. National Library of Medicine; Canadian Institutes of Health Research; National Institutes of Health; Ontario Institute for Cancer Research; Natural Sciences and Engineering Research Council of Canada; Queen's University","keywords":"Cancer; Carcinogenesis; Mutation; Biology; Identification (biology); Cancer research; Genetics; Computational biology; Bioinformatics; 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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001987245,0.0002959674,0.0007024481,0.0003269635,0.00004472815,0.00001051807,0.0001637813,0.0002940922,0.00005056062],"category_scores_gemma":[0.0001843517,0.0002581349,0.0001311089,0.000502422,0.0002021176,0.000001456324,0.00013814,0.0002184027,0.00000490764],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006360035,"about_ca_system_score_gemma":0.0001531493,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000377568,"about_ca_topic_score_gemma":0.0006266616,"domain_scores_codex":[0.9985297,0.00008541722,0.0004668509,0.0005298093,0.0001506579,0.0002375835],"domain_scores_gemma":[0.9992073,0.00005139775,0.0001590345,0.0004264564,0.00004293125,0.0001129225],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000006535613,0.00003265762,0.0000335517,0.001548129,0.0002645125,0.00007641213,0.00005901069,0.00006373679,0.0001165399,0.0002531663,0.01051451,0.9870312],"study_design_scores_gemma":[0.0004498511,0.0001155749,0.0001890626,0.002737782,0.0005716856,0.00002126855,0.00001816725,0.00002037449,0.000008783103,0.0001154794,0.9955001,0.0002518844],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.00005746339,0.9976138,0.000303066,0.0002988424,0.0003476843,0.0002280636,0.0002674843,0.00001983892,0.0008637199],"genre_scores_gemma":[0.00007856698,0.9961402,0.00008047429,0.0001374324,0.0004296385,0.0002837084,0.001805825,0.00006577864,0.0009783548],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9867793,"threshold_uncertainty_score":0.9999871,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05112187407656367,"score_gpt":0.3895419987992429,"score_spread":0.3384201247226792,"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."}}