{"id":"W2017201326","doi":"10.1007/s00180-010-0224-2","title":"Solving genetic heterogeneity in extended families by identifying sub-types of complex diseases","year":2011,"lang":"en","type":"article","venue":"Computational Statistics","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University; Université Laval; Montreal Clinical Research Institute; Université de Montréal","funders":"Canadian Institutes of Health Research","keywords":"Pedigree chart; Set (abstract data type); Genetic heterogeneity; Conditional independence; Independence (probability theory); Selection (genetic algorithm); Population; Computer science; Class (philosophy); Genetics; Data mining; Machine learning; Biology; Artificial intelligence; Statistics; Mathematics; Phenotype; Gene; Medicine","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":[0.006869209,0.0009789398,0.002427346,0.002733526,0.0009204448,0.001948299,0.00212528,0.001572801,0.00236485],"category_scores_gemma":[0.03235178,0.001158218,0.002396164,0.002581048,0.001257241,0.002196045,0.002205312,0.00169004,0.0001411345],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008505413,"about_ca_system_score_gemma":0.001776176,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01175615,"about_ca_topic_score_gemma":0.009892032,"domain_scores_codex":[0.9972305,0.001560645,0.0001537337,0.0006645634,0.0001891187,0.0002014568],"domain_scores_gemma":[0.9432114,0.05139018,0.002084675,0.001873581,0.0006555712,0.0007847028],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0006706878,0.000259624,0.1393186,0.0001820142,0.001489939,0.00125103,0.0003901627,0.786565,0.000812606,0.02658457,0.002611307,0.03986442],"study_design_scores_gemma":[0.00007071071,0.00002591106,0.00427404,0.00001067199,0.0001202632,0.0001980734,0.00007543781,0.9361433,0.0001316477,0.05870892,0.0002285266,0.00001248301],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.5573152,0.0006923312,0.438117,0.001419439,0.00003798605,0.00008659725,0.001186513,0.0003851232,0.0007598366],"genre_scores_gemma":[0.9189007,0.0002916441,0.07782599,0.0002212922,0.0001295044,0.0001194759,0.001639941,0.00006961899,0.0008018829],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.01175615,"threshold_uncertainty_score":0.03632832,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03754115334339157,"score_gpt":0.2932779172263226,"score_spread":0.255736763882931,"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."}}