{"id":"W2952371697","doi":"10.1186/s13029-018-0069-6","title":"Simulating pedigrees ascertained for multiple disease-affected relatives","year":2018,"lang":"en","type":"article","venue":"Source Code for Biology and Medicine","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canada's Michael Smith Genome Sciences Centre; University of British Columbia; Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Pedigree chart; Anticipation (artificial intelligence); Disease; Identification (biology); Cluster (spacecraft); Computer science; Medicine; Genetics; Biology; Machine learning; Pathology","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":[0.003536871,0.0004098563,0.0008212701,0.0007467172,0.0004302387,0.0007767713,0.001486396,0.001201381,0.005597999],"category_scores_gemma":[0.02098871,0.0004515956,0.0009802454,0.001091031,0.0005940667,0.0007240857,0.0008627348,0.0009481758,0.0003338401],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001142849,"about_ca_system_score_gemma":0.001061187,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02548836,"about_ca_topic_score_gemma":0.01785809,"domain_scores_codex":[0.9989564,0.0006883037,0.00004736548,0.0001596408,0.0000658499,0.00008229494],"domain_scores_gemma":[0.9790172,0.01782069,0.00101679,0.0009303379,0.0006337393,0.0005812775],"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":[0.0002787432,0.00005797997,0.02521309,0.00004733536,0.0001014072,0.0002458256,0.0001595945,0.9602867,0.0002304963,0.007830541,0.001058682,0.004489596],"study_design_scores_gemma":[0.0001692064,0.00005075949,0.002482991,0.00002506224,0.00005664262,0.000114309,0.00006893758,0.9881392,0.0001634943,0.007832247,0.0008818529,0.00001519464],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8746318,0.0004850608,0.114761,0.001130172,0.0000876721,0.0001790156,0.003315143,0.0005044325,0.004905612],"genre_scores_gemma":[0.9619606,0.0002437845,0.03273987,0.0002063703,0.00002406004,0.0002518141,0.002419367,0.00006399914,0.002090191],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02548836,"threshold_uncertainty_score":0.05067998,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02394165909793097,"score_gpt":0.3399856114480491,"score_spread":0.3160439523501181,"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."}}