{"id":"W2046421695","doi":"10.1038/gim.2014.191","title":"Whole-exome sequencing in undiagnosed genetic diseases: interpreting 119 trios","year":2015,"lang":"en","type":"article","venue":"Genetics in Medicine","topic":"Genomics and Rare Diseases","field":"Biochemistry, Genetics and Molecular Biology","cited_by":342,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"National Institute of Allergy and Infectious Diseases; National Institute of Neurological Disorders and Stroke; National Institute of General Medical Sciences; National Institute of Mental Health; National Heart, Lung, and Blood Institute; National Institute on Aging; U.S. Public Health Service","keywords":"Exome sequencing; Genetics; Exome; In silico; Biology; Gene; Genotype-phenotype distinction; Genotype; Disease; Phenotype; Candidate gene; Population; DNA sequencing; Computational biology; Bioinformatics; Medicine; Pathology","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":[0.001118992,0.0004056977,0.0004201989,0.001948693,0.0007151446,0.0008611169,0.0003486428,0.0005026605,0.001734542],"category_scores_gemma":[0.003617959,0.0001783594,0.0003378562,0.00122834,0.0005119904,0.0003248871,0.001017127,0.0004184315,0.0005650556],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002668102,"about_ca_system_score_gemma":0.0003833477,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001075721,"about_ca_topic_score_gemma":0.001778537,"domain_scores_codex":[0.9990873,0.0002221019,0.0001634254,0.0002434216,0.0001867028,0.0000969852],"domain_scores_gemma":[0.9982845,0.0008282187,0.0003311649,0.0002067805,0.0001767817,0.0001724264],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0006158391,0.00008717728,0.9421341,0.00007385605,0.000094339,0.01415456,0.0006832865,0.0004491559,0.02295799,0.0002855033,0.0008232173,0.01764086],"study_design_scores_gemma":[0.00007679148,0.0003927761,0.9116486,0.00008989931,0.0003414431,0.05695229,0.001853222,0.004703979,0.01629433,0.002044958,0.005562401,0.0000392792],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9967018,0.0002322673,0.001676128,0.00006830692,0.00001159039,0.00003098566,0.0006225222,0.00002222464,0.0006340691],"genre_scores_gemma":[0.995358,0.0002587169,0.002777679,0.0001006774,0.00001624697,0.00002307399,0.00125587,0.00002529402,0.0001842984],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001948693,"threshold_uncertainty_score":0.005917847,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02448186894664766,"score_gpt":0.2921852440222767,"score_spread":0.2677033750756291,"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."}}