{"id":"W2116887561","doi":"10.1093/bioinformatics/btl553","title":"Dependence network modeling for biomarker identification","year":2006,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Biomarker discovery; Biomarker; Identification (biology); Computational biology; Consistency (knowledge bases); Cancer; Cancer biomarkers; Microarray analysis techniques; Computer science; Microarray; Relevance (law); Data mining; Bioinformatics; Gene; Biology; Proteomics; Artificial intelligence; Genetics; Gene expression","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":[0.0001791325,0.000080812,0.00005767089,0.00002788231,0.000105169,0.00004304253,0.0001240401,0.00009205154,0.00000423057],"category_scores_gemma":[0.00002344416,0.00007622666,0.00005411322,0.0000784393,0.00001584168,0.000008463998,0.00002847336,0.00002224541,0.00001237716],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001086287,"about_ca_system_score_gemma":0.00003881243,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000428946,"about_ca_topic_score_gemma":0.000007759482,"domain_scores_codex":[0.999334,0.000008631125,0.0002853956,0.0001190244,0.00009490571,0.0001580015],"domain_scores_gemma":[0.9995108,0.00000453907,0.0001110666,0.0002484674,0.00009616632,0.00002900642],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001723823,0.00009002523,0.001295434,0.000163424,0.00004467625,1.707838e-7,0.0000741279,0.0761092,0.7054281,0.005440546,0.1759561,0.03522592],"study_design_scores_gemma":[0.0005430302,0.00004761866,0.001323079,0.00002067307,0.00002099117,0.000004768789,0.00009600296,0.8604364,0.05215607,0.001954746,0.08312326,0.0002734002],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03862838,0.0002956468,0.9579899,0.00009199853,0.0002729078,0.0002674963,0.00001290078,0.00002280633,0.002417901],"genre_scores_gemma":[0.9793618,0.000048124,0.01878604,0.0001311423,0.0002842906,0.00007296285,0.0004162765,0.00001172877,0.00088767],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9407334,"threshold_uncertainty_score":0.3108433,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02297576616951299,"score_gpt":0.2683369663905739,"score_spread":0.2453612002210609,"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."}}