{"id":"W2109641307","doi":"10.1093/bioinformatics/18.6.894","title":"DMLE+: Bayesian linkage disequilibrium gene mapping","year":2002,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":137,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"National Institutes of Health; Canadian Institutes of Health Research; Killam Trusts; National Human Genome Research Institute; Fondation pour la Recherche Médicale","keywords":"Linkage disequilibrium; Bayesian probability; Linkage (software); Disequilibrium; Computational biology; Genetics; Computer science; Gene; Biology; Artificial intelligence; Haplotype; Allele; Medicine","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":[],"consensus_categories":[],"category_scores_codex":[0.0001951193,0.0001474338,0.0001640986,0.00004957373,0.0001039612,0.00002528008,0.0001931083,0.000199106,0.0001129207],"category_scores_gemma":[0.0001612244,0.0001385269,0.0001026164,0.0001010448,0.00005360459,0.000006081109,0.0001170298,0.00007853182,0.0001906681],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001660173,"about_ca_system_score_gemma":0.00001780245,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003504739,"about_ca_topic_score_gemma":0.000002967927,"domain_scores_codex":[0.9989797,0.00003552741,0.0004026933,0.0001476347,0.00009776822,0.0003366873],"domain_scores_gemma":[0.9992978,0.00001631859,0.000153516,0.0003733645,0.00005115382,0.0001078261],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00004836782,0.0006386666,0.2102219,0.0004916049,0.0008806903,0.00002088843,0.00473398,0.002029627,0.1271221,0.0009582168,0.5554442,0.09740982],"study_design_scores_gemma":[0.002776301,0.0006708601,0.08115482,0.00005099781,0.00009084434,0.0001401872,0.0009547249,0.2864371,0.01065884,0.0005413462,0.6148931,0.001630812],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8273966,0.001656512,0.1194865,0.001629844,0.0007277995,0.0004691439,0.0001007772,0.00009798523,0.04843479],"genre_scores_gemma":[0.9453633,0.0005436721,0.04868794,0.001280185,0.0004048575,0.00001798626,0.0002302791,0.00002388431,0.00344792],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2844075,"threshold_uncertainty_score":0.5648962,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01966016657473886,"score_gpt":0.2320162652202325,"score_spread":0.2123560986454937,"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."}}