{"id":"W2272951769","doi":"10.1038/gim.2015.137","title":"Computational evaluation of exome sequence data using human and model organism phenotypes improves diagnostic efficiency","year":2015,"lang":"en","type":"article","venue":"Genetics in Medicine","topic":"Genomics and Rare Diseases","field":"Biochemistry, Genetics and Molecular Biology","cited_by":112,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Hospital for Sick Children","funders":"National Human Genome Research Institute; Common Fund; National Institutes of Health; NIH Office of the Director; Wellcome Trust","keywords":"Phenotype; Exome sequencing; Exome; Disease; Computational biology; Genetics; Gene; Medical genetics; Bioinformatics; Medicine; Biology; 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":[0.002788532,0.0007456921,0.0007781561,0.001041883,0.0003988365,0.001213011,0.0007917537,0.0006258126,0.001848882],"category_scores_gemma":[0.01482909,0.0002820357,0.0007711235,0.001017103,0.0004079517,0.000982489,0.0007344245,0.0006641555,0.0002222154],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009953271,"about_ca_system_score_gemma":0.001172279,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008212951,"about_ca_topic_score_gemma":0.01203693,"domain_scores_codex":[0.9990724,0.0005217539,0.00007194442,0.0001858282,0.0001100584,0.00003792322],"domain_scores_gemma":[0.994079,0.004945007,0.0002098421,0.0004055688,0.000259068,0.0001015553],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.001699424,0.0004642571,0.05768742,0.0003318804,0.0004659364,0.0003380066,0.0001260937,0.8836244,0.003762519,0.001787742,0.002767017,0.04694534],"study_design_scores_gemma":[0.00008129268,0.0001039734,0.004049548,0.00001174959,0.00003826921,0.00008090705,0.00004091006,0.9920828,0.001296397,0.00184321,0.0003616584,0.000009129139],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9498289,0.0004299438,0.04254482,0.000822318,0.00004557997,0.0001059431,0.002782493,0.001667847,0.001772128],"genre_scores_gemma":[0.941259,0.0001586079,0.05346174,0.0001661128,0.00001390362,0.00007747045,0.004505055,0.00007162776,0.0002863783],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008212951,"threshold_uncertainty_score":0.0163303,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.148278778509386,"score_gpt":0.3878168084871614,"score_spread":0.2395380299777755,"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."}}