{"id":"W2554121312","doi":"10.1186/s12919-016-0007-z","title":"Genetic Analysis Workshop 19: methods and strategies for analyzing human sequence and gene expression data in extended families and unrelated individuals","year":2016,"lang":"en","type":"article","venue":"BMC Proceedings","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Jewish General Hospital","funders":"National Institute of General Medical Sciences; National Institute of Diabetes and Digestive and Kidney Diseases; National Heart, Lung, and Blood Institute; National Institutes of Health","keywords":"Sequence (biology); Exome; Data science; Computational biology; Sample (material); Gene sequence; Genetic data; Exome sequencing; Bioinformatics; Data mining; Gene; Medicine; Computer science; Genetics; Biology; Mutation; Population","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":[0.05574086,0.001591187,0.002007885,0.003663281,0.001778691,0.004884856,0.003882167,0.002384917,0.007962568],"category_scores_gemma":[0.05156675,0.001672159,0.003136448,0.002957657,0.001814991,0.001688116,0.004697908,0.004495795,0.004909977],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001238088,"about_ca_system_score_gemma":0.005753082,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004112688,"about_ca_topic_score_gemma":0.005309656,"domain_scores_codex":[0.9803915,0.01318116,0.001200524,0.002308574,0.002282766,0.000635515],"domain_scores_gemma":[0.9661723,0.02250368,0.0008157922,0.005310317,0.003560238,0.001637787],"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":[0.001029364,0.0004487991,0.02014459,0.001377791,0.001838503,0.002738954,0.003489022,0.009849646,0.06373775,0.09116047,0.1450159,0.6591692],"study_design_scores_gemma":[0.0007930876,0.0008919178,0.06062881,0.0008311091,0.0008732017,0.003821275,0.001697381,0.0645974,0.0762441,0.2182048,0.5705355,0.000881398],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006668299,0.0005251938,0.9787049,0.003308818,0.0005176425,0.0006173541,0.004225066,0.002904698,0.002527988],"genre_scores_gemma":[0.03405162,0.000581813,0.9417439,0.002010088,0.0003471647,0.002628096,0.006720221,0.00263912,0.009277957],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.05574086,"threshold_uncertainty_score":0.2947894,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06896013310767928,"score_gpt":0.3888711877261319,"score_spread":0.3199110546184526,"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."}}