{"id":"W4229072282","doi":"10.1126/sciadv.abj1624","title":"CancerVar: An artificial intelligence–empowered platform for clinical interpretation of somatic mutations in cancer","year":2022,"lang":"en","type":"article","venue":"Science Advances","topic":"Cancer Genomics and Diagnostics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":62,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre","funders":"U.S. National Library of Medicine; National Institute of General Medical Sciences","keywords":"Somatic cell; Computer science; Consistency (knowledge bases); Artificial intelligence; Machine learning; Germline mutation; Computational biology; Bioinformatics; Mutation; Biology; Genetics; Gene","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":[0.004829871,0.001531839,0.001043636,0.006129307,0.0005782822,0.003140445,0.002963301,0.001719285,0.006949022],"category_scores_gemma":[0.0252788,0.0008938515,0.001386651,0.002859037,0.0005893935,0.002533337,0.005487473,0.002675702,0.003931195],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00139791,"about_ca_system_score_gemma":0.003864977,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007634439,"about_ca_topic_score_gemma":0.01456387,"domain_scores_codex":[0.9968055,0.0009078713,0.000447459,0.0008013327,0.0008940484,0.000143663],"domain_scores_gemma":[0.9907023,0.005455059,0.0008182332,0.001399264,0.001131649,0.0004935912],"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":[0.001431788,0.0006530429,0.02723973,0.003071501,0.001041429,0.001951719,0.00126707,0.03919178,0.01169756,0.01805129,0.2646433,0.6297598],"study_design_scores_gemma":[0.000625142,0.0003735257,0.01412129,0.001502887,0.0005157508,0.001999263,0.0005159968,0.5083979,0.03131292,0.1130294,0.3271818,0.0004240612],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.026898,0.006677111,0.5746229,0.007746241,0.0005796675,0.00168146,0.06774794,0.2988915,0.01515517],"genre_scores_gemma":[0.1878111,0.003477688,0.687245,0.00449681,0.000265148,0.00134723,0.1030973,0.007278988,0.004980739],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007634439,"threshold_uncertainty_score":0.02554309,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0513542110605231,"score_gpt":0.4123568745778924,"score_spread":0.3610026635173693,"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."}}