{"id":"W2009255619","doi":"10.1073/pnas.0809444106","title":"Prognostic gene signatures for non-small-cell lung cancer","year":2009,"lang":"en","type":"article","venue":"Proceedings of the National Academy of Sciences","topic":"Cancer Genomics and Diagnostics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":207,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre; University Health Network; University of Toronto; Ontario Institute for Cancer Research","funders":"Canadian Institutes of Health Research; National Cancer Institute; Princess Margaret Hospital Foundation; Genome Canada; University of Pennsylvania","keywords":"Gene signature; Lung cancer; Oncology; Biology; Computational biology; Microarray; Microarray analysis techniques; Stage (stratigraphy); Gene expression profiling; Gene; Bioinformatics; Internal medicine; Medicine; Gene expression; Genetics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001224383,0.0002726967,0.000521339,0.00081066,0.0002276421,0.0004030204,0.0003844196,0.0004367873,0.0007361314],"category_scores_gemma":[0.004800383,0.0001113358,0.0004651687,0.0006464047,0.0002563088,0.0004315233,0.0004426729,0.0004396752,0.0002828098],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002818991,"about_ca_system_score_gemma":0.0003363542,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002370817,"about_ca_topic_score_gemma":0.0004869087,"domain_scores_codex":[0.9994554,0.0001897939,0.00004428319,0.0001030617,0.0001387397,0.00006876988],"domain_scores_gemma":[0.9978594,0.001098169,0.0003989306,0.000352854,0.0001679289,0.00012267],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.003792969,0.001091248,0.5527846,0.0002981311,0.0003730692,0.0006869506,0.000132691,0.05493917,0.1693965,0.001488391,0.003760933,0.2112554],"study_design_scores_gemma":[0.0003296887,0.00163617,0.5985102,0.00002619516,0.0002240178,0.002363093,0.0001017453,0.3267646,0.05620227,0.009631945,0.004119438,0.00009070596],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9925444,0.0002107363,0.005102183,0.0001581761,0.00001201887,0.00003157969,0.001521194,0.0001077053,0.0003119885],"genre_scores_gemma":[0.990393,0.00005159489,0.005110136,0.00003670473,0.00001389969,0.0000283544,0.004190151,0.000007757578,0.0001683138],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001224383,"threshold_uncertainty_score":0.00647521,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01717273274080997,"score_gpt":0.2929601469341451,"score_spread":0.2757874141933351,"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."}}