{"id":"W2137777820","doi":"10.1186/1753-6561-8-s1-s1","title":"Genetic Analysis Workshop 18: Methods and strategies for analyzing human sequence and phenotype data in members of extended pedigrees","year":2014,"lang":"en","type":"article","venue":"BMC Proceedings","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; McMaster University","funders":"Eunice Kennedy Shriver National Institute of Child Health and Human Development; National Institute of General Medical Sciences; National Human Genome Research Institute; National Heart, Lung, and Blood Institute; Goddard Space Flight Center; National Institute on Aging; National Institutes of Health","keywords":"Pedigree chart; Imputation (statistics); Genotyping; Whole genome sequencing; Data science; Sequence (biology); Computational biology; 1000 Genomes Project; Data mining; Genetics; Bioinformatics; Genome; Genotype; Computer science; Missing data; Biology; Machine learning; Gene; Single-nucleotide polymorphism","routes":{"ca_aff":true,"ca_fund":false,"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":[0.08277602,0.00207779,0.00198715,0.00362461,0.001609425,0.004780217,0.003685688,0.002768574,0.01417175],"category_scores_gemma":[0.07598484,0.002238904,0.003787384,0.00231385,0.00187704,0.003017915,0.005911912,0.005486852,0.00697176],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001058024,"about_ca_system_score_gemma":0.004416256,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001867022,"about_ca_topic_score_gemma":0.00242728,"domain_scores_codex":[0.9691301,0.02228749,0.001675173,0.002989352,0.003110998,0.0008069135],"domain_scores_gemma":[0.9472285,0.03766575,0.001200932,0.005958839,0.005631723,0.002314415],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0008750763,0.0004381718,0.01122728,0.001439576,0.001502481,0.001899152,0.004083792,0.01181838,0.02403987,0.1125071,0.1831847,0.6469844],"study_design_scores_gemma":[0.0007347643,0.0006038548,0.016936,0.0009798578,0.0005403831,0.00265784,0.001414842,0.06745742,0.02655659,0.3329214,0.5485992,0.0005978556],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001787409,0.000440161,0.990243,0.002549359,0.000456731,0.0003395687,0.001141621,0.001801139,0.001240972],"genre_scores_gemma":[0.01488945,0.0005545005,0.9714962,0.001466027,0.0004120725,0.001549573,0.002574773,0.001887434,0.00517009],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.08277602,"threshold_uncertainty_score":0.4377667,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07825195354701643,"score_gpt":0.3989627051897927,"score_spread":0.3207107516427763,"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."}}