{"id":"W2008546211","doi":"10.1038/sj.bjc.6604746","title":"Robust prognostic value of a knowledge-based proliferation signature across large patient microarray studies spanning different cancer types","year":2008,"lang":"en","type":"article","venue":"British Journal of Cancer","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":55,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre; Ontario Institute for Cancer Research","funders":"","keywords":"Gene signature; Proportional hazards model; Microarray; Microarray analysis techniques; Oncology; Breast cancer; Cancer; Multivariate statistics; Bioinformatics; Survival analysis; Biology; Medicine; Internal medicine; Computational biology; Gene expression; Computer science; Gene; Machine learning; Genetics","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":[0.006327222,0.0003778352,0.001071418,0.002280083,0.0004364515,0.001705247,0.0004520314,0.0009157499,0.0007352067],"category_scores_gemma":[0.02075941,0.0002080096,0.0005100403,0.00233502,0.0005812121,0.0009344692,0.0008488423,0.0006186683,0.0002600996],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005073732,"about_ca_system_score_gemma":0.0004296967,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006359069,"about_ca_topic_score_gemma":0.001013802,"domain_scores_codex":[0.9965407,0.001449249,0.0003629797,0.0007429614,0.0006512732,0.0002528615],"domain_scores_gemma":[0.978837,0.01492992,0.002796481,0.002014331,0.001004656,0.000417468],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001900054,0.0001482365,0.8991542,0.0001926153,0.001371009,0.0002743166,0.0001519088,0.006420084,0.01839499,0.0002433297,0.0008948029,0.07085449],"study_design_scores_gemma":[0.00007671553,0.0005660375,0.9630198,0.00004511835,0.0008840128,0.001030412,0.0002043123,0.02357969,0.00705362,0.002086217,0.00138928,0.00006459651],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9894356,0.001692918,0.00613863,0.0003482151,0.00002998081,0.0000284953,0.001452405,0.00008224436,0.0007916181],"genre_scores_gemma":[0.9952656,0.0001966769,0.002698333,0.00005690777,0.00003658312,0.00002290536,0.001628952,0.000007581793,0.00008646002],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006327222,"threshold_uncertainty_score":0.03346199,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02607840159457887,"score_gpt":0.3049250044833589,"score_spread":0.27884660288878,"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."}}