{"id":"W2050870676","doi":"10.1186/1471-2105-10-410","title":"Refining gene signatures: a Bayesian approach","year":2009,"lang":"en","type":"article","venue":"BMC Bioinformatics","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Health Canada; Douglas Mental Health University Institute; Natural Sciences and Engineering Research Council of Canada","funders":"Canadian Institutes of Health Research","keywords":"Computer science; Bayesian probability; Context (archaeology); Data mining; Machine learning; DNA microarray; Artificial intelligence; Curse of dimensionality; Identification (biology); Microarray analysis techniques; Feature selection; Bayes' theorem; Pattern recognition (psychology); Gene; Biology; Genetics; Gene expression","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":[0.005284556,0.0007533582,0.001669761,0.002542908,0.0006973472,0.001020303,0.002455198,0.001473042,0.001474514],"category_scores_gemma":[0.0155142,0.0008237954,0.001481927,0.001902293,0.00146182,0.00140148,0.001338538,0.001860939,0.0006251643],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001495742,"about_ca_system_score_gemma":0.00187608,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003980222,"about_ca_topic_score_gemma":0.005761415,"domain_scores_codex":[0.9975055,0.001007081,0.0001107453,0.0004755538,0.0007657159,0.0001355143],"domain_scores_gemma":[0.9911874,0.006405756,0.0005781467,0.0006689221,0.0009923103,0.0001675423],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000405338,0.0001823627,0.007367944,0.000251596,0.000243138,0.0001787126,0.0002912038,0.596279,0.01110615,0.05214852,0.003718885,0.3278271],"study_design_scores_gemma":[0.00003827733,0.00004156673,0.0008405175,0.00002926834,0.00004765708,0.00007610178,0.00001630827,0.9384618,0.001942831,0.05682676,0.001651078,0.00002774355],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.008014563,0.0001582925,0.9908235,0.0002548828,0.000009365189,0.00003226902,0.0000682246,0.000207292,0.0004315695],"genre_scores_gemma":[0.2767767,0.0004336957,0.7192591,0.0005206098,0.0001387869,0.0003581289,0.0005906161,0.0001865943,0.001735788],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005284556,"threshold_uncertainty_score":0.02794778,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01636014766742929,"score_gpt":0.250817003064846,"score_spread":0.2344568553974167,"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."}}