{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004874427,0.001402016,0.001765342,0.002405787,0.0007136362,0.001178239,0.002202794,0.001364196,0.00362213],"category_scores_gemma":[0.008763281,0.0008210302,0.001341399,0.001913276,0.0004617938,0.001827413,0.001464862,0.001840275,0.001999667],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009363269,"about_ca_system_score_gemma":0.002061638,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01415038,"about_ca_topic_score_gemma":0.01917717,"domain_scores_codex":[0.9973347,0.001102371,0.0001321736,0.000533407,0.0007430185,0.0001543024],"domain_scores_gemma":[0.9961879,0.00277116,0.0002326129,0.0002491653,0.0004344157,0.0001248037],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001197711,0.0004116227,0.01446774,0.0003908302,0.000551781,0.0004468902,0.0001894739,0.2589066,0.01092992,0.01057636,0.02569254,0.6762385],"study_design_scores_gemma":[0.0001047536,0.00003822563,0.001215776,0.00003229313,0.00006143736,0.000120287,0.00001217644,0.9787359,0.002870346,0.01258573,0.004183951,0.00003920459],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01190436,0.0007288791,0.9784809,0.0002830137,0.00008838083,0.0001074959,0.0009645891,0.006310992,0.001131333],"genre_scores_gemma":[0.1756498,0.0008630618,0.8113166,0.0006881336,0.0002014395,0.000393631,0.005737228,0.0008204214,0.004329651],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01415038,"threshold_uncertainty_score":0.02813601,"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."}}