{"id":"W2130948145","doi":"10.1186/1471-2105-7-448","title":"Gene function classification using Bayesian models with hierarchy-based priors","year":2006,"lang":"en","type":"article","venue":"BMC 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 Toronto; Toronto Public Health","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Prior probability; Bayesian probability; Hierarchy; DNA microarray; Computational biology; Computer science; Function (biology); Artificial intelligence; Data mining; Biology; Gene; 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":[0.008705382,0.001200763,0.001418957,0.002365135,0.0006969054,0.001848708,0.002273716,0.001941829,0.002431089],"category_scores_gemma":[0.02065725,0.0008414091,0.001806776,0.001884801,0.001073401,0.00275665,0.001254093,0.002190202,0.001232076],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002687725,"about_ca_system_score_gemma":0.001515322,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02133948,"about_ca_topic_score_gemma":0.01667856,"domain_scores_codex":[0.9975974,0.001241651,0.0001028648,0.0004134255,0.0004525204,0.0001921302],"domain_scores_gemma":[0.9848899,0.01251699,0.0009363883,0.0005435487,0.0008672962,0.0002458293],"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":[0.0005641174,0.0002025649,0.01061725,0.000141562,0.0001621324,0.00007650199,0.0002232861,0.8696237,0.001267745,0.01595362,0.002414217,0.09875336],"study_design_scores_gemma":[0.00002398748,0.00001833573,0.0005554065,0.00001199767,0.00001294519,0.00001244993,0.000005945773,0.9881661,0.0001527055,0.01084143,0.0001889153,0.000009818733],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.08843087,0.0007043128,0.9065581,0.001014962,0.00004044059,0.0001465411,0.0006027657,0.0007804691,0.001721417],"genre_scores_gemma":[0.7290242,0.0006699551,0.2643316,0.0004143071,0.0001989546,0.0004795484,0.001908561,0.0001303888,0.00284232],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02133948,"threshold_uncertainty_score":0.04603899,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0276466903293348,"score_gpt":0.2413619379869356,"score_spread":0.2137152476576009,"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."}}