{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006132193,0.00008137863,0.0001138278,0.0000453943,0.00002946482,0.000004785116,0.0001836235,0.00004869692,0.000003273141],"category_scores_gemma":[0.0005530712,0.00007286126,0.000006844621,0.00005980392,0.0001640001,0.000002696536,0.0001769508,0.00003310092,1.948252e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001568736,"about_ca_system_score_gemma":0.0002511037,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003979401,"about_ca_topic_score_gemma":0.00002207118,"domain_scores_codex":[0.9991469,0.00004342209,0.0002069701,0.0002473814,0.0002557758,0.00009949956],"domain_scores_gemma":[0.9993247,0.0000220143,0.00007586846,0.0002861279,0.0002243835,0.00006688781],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001619926,0.00009261908,0.008254067,0.0000448116,0.00002204447,0.000003045245,0.0004586862,0.3394893,0.6484526,0.0001444867,0.0001617824,0.002860333],"study_design_scores_gemma":[0.001412438,0.0002379654,0.008136958,0.00004299478,0.0001102989,0.0000128162,0.0002339458,0.9792565,0.00370688,0.006680483,0.00003145186,0.0001372513],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9878204,0.005828147,0.005997288,0.00004042247,0.0000618271,0.0001550026,0.00003744777,0.000001919718,0.00005750987],"genre_scores_gemma":[0.9943227,0.0001510651,0.004971317,0.00006156909,0.0000905089,0.000002446932,0.0003857229,0.000009601661,0.000005021816],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6447458,"threshold_uncertainty_score":0.2971196,"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."}}