{"id":"W2075036386","doi":"10.1002/gepi.20253","title":"Haplotype inference using a Bayesian Hidden Markov model","year":2007,"lang":"en","type":"article","venue":"Genetic Epidemiology","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hospital for Sick Children; University of Toronto","funders":"Hospital for Sick Children; Natural Sciences and Engineering Research Council of Canada; Mitacs; Canada Research Chairs","keywords":"Haplotype; Haplotype estimation; International HapMap Project; Linkage disequilibrium; Population; Hidden Markov model; Markov chain Monte Carlo; Bayesian probability; Genetics; Biology; Algorithm; Computer science; Artificial intelligence; Allele","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":[0.002805101,0.0003212385,0.0005746414,0.0001223435,0.000179862,0.00000603504,0.0003970844,0.000655936,0.00005880499],"category_scores_gemma":[0.003275658,0.0003158624,0.0001947115,0.0001602983,0.0002425596,0.000003158275,0.0002569143,0.000224191,0.00003778069],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005666694,"about_ca_system_score_gemma":0.000206494,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001439017,"about_ca_topic_score_gemma":0.0002381969,"domain_scores_codex":[0.9965873,0.0004891018,0.0009744181,0.0007754362,0.00009827486,0.001075512],"domain_scores_gemma":[0.9980324,0.0004518855,0.0003727396,0.0007159386,0.0001499544,0.0002770853],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001669773,0.0001136266,0.8167111,0.00002719164,0.0002178372,0.00001282966,0.0000998555,0.02714958,0.08175106,0.0009256555,0.005849464,0.06697485],"study_design_scores_gemma":[0.001382375,0.0007932652,0.642461,0.00003003622,0.0001656232,0.0002080912,0.0001573813,0.314108,0.002425058,0.02962277,0.007275278,0.001371074],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4821594,0.001066235,0.5149054,0.0003058318,0.0002035544,0.000158517,0.00001060446,0.00002208738,0.001168342],"genre_scores_gemma":[0.6526957,0.0002603357,0.344157,0.001983555,0.0003431825,0.00001249291,0.00005546394,0.00003417169,0.0004580842],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.2869585,"threshold_uncertainty_score":0.9999294,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04513488346860547,"score_gpt":0.3411188515923508,"score_spread":0.2959839681237453,"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."}}