{"id":"W2076557910","doi":"10.1002/gepi.20320","title":"Using disease symptoms to improve detection of linkage under genetic heterogeneity","year":2008,"lang":"en","type":"article","venue":"Genetic Epidemiology","topic":"Autism Spectrum Disorder Research","field":"Neuroscience","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"National Institute of Mental Health; Canadian Institutes of Health Research; National Institutes of Health","keywords":"Latent class model; Genetic heterogeneity; Local independence; Disease; Genetic linkage; Linkage (software); Identity by descent; Genetics; Autism; Phenotype; Latent variable; Psychology; Latent variable model; Biology; Gene; Computer science; Medicine; Developmental psychology; Artificial intelligence; 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":[0.02766105,0.0009700195,0.001483523,0.003181963,0.0006755557,0.001777131,0.001240662,0.001811908,0.001512377],"category_scores_gemma":[0.1307939,0.0006371035,0.001126672,0.002103733,0.002012763,0.0028556,0.003121891,0.001789496,0.0002203984],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005576178,"about_ca_system_score_gemma":0.0008040445,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002042483,"about_ca_topic_score_gemma":0.001630217,"domain_scores_codex":[0.9742241,0.02110668,0.0006222769,0.002508972,0.00112236,0.000415693],"domain_scores_gemma":[0.8483483,0.135692,0.005636217,0.007514063,0.001801601,0.001007765],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.002788692,0.0004531486,0.4686825,0.0005119971,0.001885814,0.001198876,0.002547952,0.103377,0.01472342,0.03194137,0.001892506,0.3699968],"study_design_scores_gemma":[0.0007496171,0.001563999,0.1933592,0.00008395493,0.0008346859,0.002435969,0.0004880515,0.6780612,0.0110119,0.1077354,0.003352913,0.000323008],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.42339,0.0006077716,0.5713139,0.0008976519,0.00005347326,0.0002427925,0.0002962117,0.0006642095,0.002534044],"genre_scores_gemma":[0.8896923,0.0001559506,0.1092076,0.0001698615,0.00004370271,0.0001161706,0.0002231468,0.00006878103,0.0003225441],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02766105,"threshold_uncertainty_score":0.1462873,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.125897743595005,"score_gpt":0.3722373801946607,"score_spread":0.2463396365996557,"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."}}