{"id":"W2162797435","doi":"10.1093/bioinformatics/btl233","title":"BNTagger: improved tagging SNP selection using Bayesian networks","year":2006,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Machine Learning in Bioinformatics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":56,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Selection (genetic algorithm); Computer science; SNP; Bayesian probability; Artificial intelligence; Biology; Genetics; Single-nucleotide polymorphism","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002881013,0.0002631205,0.0001880489,0.0001004949,0.0002471381,0.0001197901,0.0002194756,0.0002671741,0.00002671799],"category_scores_gemma":[0.00006091466,0.0002576138,0.0001151662,0.0002181672,0.00006553656,0.00002743034,0.0001308642,0.0002195815,0.0000158248],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005395166,"about_ca_system_score_gemma":0.00008073329,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008476362,"about_ca_topic_score_gemma":0.0000399426,"domain_scores_codex":[0.9985498,0.00003132971,0.0006166138,0.000159074,0.0001771286,0.0004660912],"domain_scores_gemma":[0.9991205,0.00001291888,0.0003494141,0.0003332131,0.00009887193,0.00008510421],"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":[0.0003390319,0.0004029081,0.05698962,0.001003482,0.0004289512,0.000006585906,0.0007882767,0.6457323,0.1967663,0.003237125,0.04973551,0.04456992],"study_design_scores_gemma":[0.0004296386,0.0001162022,0.0005365398,0.00002186886,0.00002779612,0.00007114213,0.0000745039,0.9641691,0.005745532,0.00002393798,0.02843739,0.0003463157],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1078939,0.000137923,0.8787523,0.00006849789,0.0004341842,0.0003909075,0.00001229689,0.0001409248,0.01216907],"genre_scores_gemma":[0.8054537,0.00003459705,0.191711,0.0006960416,0.0009052273,0.00001114943,0.0004429747,0.0000576838,0.0006875864],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6975598,"threshold_uncertainty_score":0.9999876,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006642357560736971,"score_gpt":0.2410534720830236,"score_spread":0.2344111145222866,"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."}}