{"id":"W2800437105","doi":"10.1002/ajmg.b.32638","title":"Machine learning in schizophrenia genomics, a case‐control study using 5,090 exomes","year":2018,"lang":"en","type":"article","venue":"American Journal of Medical Genetics Part B Neuropsychiatric Genetics","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":47,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Fondation de l'Hôpital de Montréal pour enfants","keywords":"Exome sequencing; Exome; Genomics; Minor allele frequency; Genetics; Computational biology; Gene; Biology; Computer science; Artificial intelligence; Allele frequency; Genotype; Genome; Mutation","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":[0.003017945,0.0005559466,0.0004992856,0.001736256,0.0009692259,0.0007162055,0.0003873914,0.000688323,0.001892628],"category_scores_gemma":[0.004361373,0.0005593104,0.0008924362,0.0009948588,0.0004070159,0.0003950156,0.0005837927,0.0003866264,0.0003613506],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004925376,"about_ca_system_score_gemma":0.000359089,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003093504,"about_ca_topic_score_gemma":0.004303953,"domain_scores_codex":[0.9989433,0.0003646212,0.00009802217,0.0003437941,0.0001646253,0.00008573389],"domain_scores_gemma":[0.999149,0.0003121475,0.0001336719,0.0002481419,0.00007438751,0.00008270684],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.005328982,0.001374541,0.9312188,0.00023778,0.001677303,0.003285841,0.0006506207,0.001422745,0.02530692,0.0008370484,0.001207299,0.02745207],"study_design_scores_gemma":[0.0004671809,0.001255724,0.982527,0.00004010613,0.0006829838,0.00401515,0.0001974823,0.004922198,0.002477233,0.0005172967,0.002861075,0.00003658376],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9973827,0.0002414588,0.00152089,0.00004613862,0.000007631296,0.00005314135,0.0004860129,0.0000154312,0.0002465811],"genre_scores_gemma":[0.9946641,0.0001404662,0.003004128,0.00006056369,0.00001797088,0.00006981323,0.001664062,0.00001141261,0.0003675078],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003093504,"threshold_uncertainty_score":0.01596063,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01465495215126281,"score_gpt":0.2925304663366435,"score_spread":0.2778755141853806,"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."}}