{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002882215,0.001186425,0.001076179,0.00306847,0.0004969005,0.001001565,0.001519929,0.001091575,0.002732875],"category_scores_gemma":[0.01349736,0.000516838,0.001139676,0.00227264,0.0009869448,0.002232521,0.0009835651,0.001269564,0.0006134108],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001591808,"about_ca_system_score_gemma":0.001024467,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005743422,"about_ca_topic_score_gemma":0.003700626,"domain_scores_codex":[0.9984927,0.0007554147,0.00006263047,0.000340224,0.0002500946,0.00009897004],"domain_scores_gemma":[0.9930507,0.005222115,0.0005913542,0.0003793927,0.0006054497,0.000151137],"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":[0.0001280967,0.00007395773,0.005820123,0.0001350333,0.000140673,0.0001413562,0.0000790885,0.8625124,0.001259487,0.0820209,0.002217274,0.04547156],"study_design_scores_gemma":[0.000003653484,0.000009523182,0.0003186589,0.000008196134,0.000009925101,0.00002352519,0.000004302722,0.9500656,0.0001785295,0.04885141,0.0005199479,0.000006631156],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01528676,0.0004211084,0.9810765,0.0005494123,0.00002864548,0.0000705743,0.0006602659,0.0003113646,0.001595379],"genre_scores_gemma":[0.7750837,0.001770469,0.2131494,0.0004440244,0.000218373,0.0007742188,0.003193468,0.0001649999,0.005201395],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005743422,"threshold_uncertainty_score":0.01524276,"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."}}