{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007201526,0.0009266726,0.002601683,0.003177482,0.001250495,0.002115698,0.003467788,0.002036216,0.006029283],"category_scores_gemma":[0.02354365,0.001668166,0.002439249,0.003094391,0.001323157,0.003931897,0.001667072,0.003089793,0.001470618],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001755971,"about_ca_system_score_gemma":0.002437851,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02212973,"about_ca_topic_score_gemma":0.02346897,"domain_scores_codex":[0.9966754,0.001927424,0.0001787285,0.0006648041,0.0003407171,0.0002129022],"domain_scores_gemma":[0.9848408,0.01313692,0.0006331654,0.0006701841,0.000508118,0.0002108703],"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":[0.0002906249,0.0001102322,0.007546198,0.0001965218,0.000396964,0.0004252191,0.0002449644,0.8376002,0.000813876,0.06142233,0.003495032,0.08745783],"study_design_scores_gemma":[0.0000398236,0.00001201935,0.0004276792,0.00001713032,0.00003503344,0.00005583223,0.00001137778,0.9379631,0.000116292,0.06076864,0.0005313136,0.00002182769],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009803821,0.0003471812,0.9877022,0.0003018301,0.00003630255,0.00005433872,0.0005978767,0.0005370636,0.000619357],"genre_scores_gemma":[0.4138582,0.001380426,0.5752856,0.0004475506,0.0002911941,0.0004706446,0.003966454,0.0002914721,0.004008548],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02212973,"threshold_uncertainty_score":0.04400188,"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."}}