{"id":"W2012673284","doi":"10.1038/nmeth.2562","title":"Computational approaches to identify functional genetic variants in cancer genomes","year":2013,"lang":"en","type":"article","venue":"Nature Methods","topic":"Cancer Genomics and Diagnostics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":178,"is_retracted":false,"has_abstract":false,"ca_institutions":"Ontario Institute for Cancer Research; University of Toronto","funders":"National Cancer Institute; National Human Genome Research Institute; Wellcome Trust","keywords":"Genome; Somatic cell; Carcinogenesis; Biology; Computational biology; Cancer; Genetics; Cancer genome sequencing; Phenotype; Mutation; Genomics; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002312579,0.0001270409,0.0001295057,0.00008267298,0.00004077109,0.00004430475,0.0001424946,0.0002476174,0.000172311],"category_scores_gemma":[0.000129263,0.000125405,0.00005347889,0.0001578679,0.00002552907,0.000003340153,0.0001095525,0.00019095,0.00002055812],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004279656,"about_ca_system_score_gemma":0.0001244948,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009116332,"about_ca_topic_score_gemma":0.00006862342,"domain_scores_codex":[0.999059,0.00009731403,0.0001764855,0.0003532644,0.0001134864,0.0002004061],"domain_scores_gemma":[0.9995379,0.00006722438,0.00004220784,0.0001845736,0.00007621728,0.00009184347],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0001536948,0.0003106303,0.05630032,0.00007081809,0.0002559119,0.000008273878,0.0003094083,0.2011587,0.3185967,0.002640426,0.0401369,0.3800583],"study_design_scores_gemma":[0.0005802779,0.00008179237,0.9380449,0.00001118403,0.00001880616,0.00001165418,0.00004038316,0.003359182,0.01141649,0.006064732,0.04004189,0.0003287198],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7694744,0.01454907,0.2114501,0.001547049,0.001240086,0.0005754963,0.00006118228,0.00001212762,0.001090506],"genre_scores_gemma":[0.6509421,0.0001536092,0.3458555,0.001904382,0.0005401291,0.0001862975,0.00009115378,0.00002300129,0.000303838],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8817446,"threshold_uncertainty_score":0.5113866,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05485723337070874,"score_gpt":0.3627371022946355,"score_spread":0.3078798689239268,"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."}}