{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003560555,0.0002356545,0.000315767,0.0002040559,0.00002930399,0.00001705984,0.0003432722,0.0001443893,0.00001964742],"category_scores_gemma":[0.0006630664,0.0002187142,0.00005688916,0.000248157,0.0001505783,0.000002938752,0.0001841744,0.0001484075,0.000009088835],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001610074,"about_ca_system_score_gemma":0.0003896516,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001312166,"about_ca_topic_score_gemma":0.0003318044,"domain_scores_codex":[0.9982098,0.0001204901,0.0005138388,0.0004972146,0.000240233,0.0004184173],"domain_scores_gemma":[0.9990036,0.00003938429,0.0001048413,0.0004703187,0.00008664355,0.0002952333],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004787472,0.000310639,0.769275,0.0001979003,0.00008695653,0.001172176,0.002930188,0.02454522,0.1784862,0.00005650733,0.008444785,0.01401568],"study_design_scores_gemma":[0.03260816,0.005999384,0.7629297,0.00235698,0.0003924053,0.0004214173,0.02295845,0.02713651,0.01906419,0.008445942,0.1138819,0.003804913],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.984639,0.01325883,0.0003606219,0.0003037099,0.0003999881,0.000312102,0.00002436928,0.00001181255,0.0006896009],"genre_scores_gemma":[0.9966683,0.0008505424,0.000821294,0.0008123596,0.0004367027,0.00004216644,0.000208997,0.00003211179,0.0001274774],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.159422,"threshold_uncertainty_score":0.8918906,"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."}}