{"id":"W1983065532","doi":"10.1371/journal.pcbi.1002875","title":"Significance Analysis of Prognostic Signatures","year":2013,"lang":"en","type":"article","venue":"PLoS Computational Biology","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"Montreal Clinical Research Institute","funders":"Klarman Family Foundation","keywords":"Breast cancer; Univariate; Gene signature; Oncology; Gene expression profiling; Survival analysis; Cancer; Recursive partitioning; Biology; Ovarian cancer; Gene; Metastasis; Bioinformatics; Internal medicine; Computational biology; Medicine; Gene expression; Genetics; Computer science; Machine learning; Multivariate statistics","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":[0.01441875,0.001106555,0.001817915,0.006559571,0.0008879505,0.002021507,0.001265662,0.0007949049,0.003388738],"category_scores_gemma":[0.06261994,0.0003497682,0.001919172,0.004062558,0.001836851,0.001629235,0.002302254,0.001826134,0.000636505],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009596893,"about_ca_system_score_gemma":0.00191789,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008498924,"about_ca_topic_score_gemma":0.0005190636,"domain_scores_codex":[0.9874052,0.005632652,0.001069528,0.002188834,0.003014812,0.0006888634],"domain_scores_gemma":[0.9459514,0.04151844,0.003571987,0.004731315,0.003519567,0.0007073488],"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":[0.001678574,0.0002420043,0.3169564,0.001547625,0.003210432,0.0009986408,0.0004695101,0.03745674,0.01509073,0.04522547,0.01157158,0.5655523],"study_design_scores_gemma":[0.0004754194,0.002061364,0.1763687,0.0002687901,0.00216803,0.0024582,0.0006446383,0.3823385,0.02205203,0.3844174,0.02651989,0.0002271154],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2756082,0.003848674,0.7005958,0.002189006,0.0006677278,0.0005798681,0.005547953,0.002900296,0.008062538],"genre_scores_gemma":[0.9368567,0.0002429974,0.05937334,0.0002542917,0.0002839899,0.00023433,0.002090374,0.0001396687,0.0005242337],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01441875,"threshold_uncertainty_score":0.07625455,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01341624485266165,"score_gpt":0.2604890887081666,"score_spread":0.247072843855505,"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."}}