{"id":"W2515610997","doi":"10.1016/j.nmd.2015.06.406","title":"Filling in the gap between exome and genome: mRNA analysis as a clinical diagnosis tool","year":2015,"lang":"en","type":"article","venue":"Neuromuscular Disorders","topic":"Molecular Biology Techniques and Applications","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Hospital for Sick Children","funders":"","keywords":"Missense mutation; Genetics; Exon; Exome sequencing; Biology; Exon skipping; Gene; splice; Exome; Duchenne muscular dystrophy; Multiplex ligation-dependent probe amplification; RNA splicing; Muscular dystrophy; Titin; Dystrophin; Mutation; RNA; Alternative splicing","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":[0.0006783876,0.0001584752,0.0002219232,0.00008633896,0.00007185512,0.0000330019,0.00032734,0.0001892851,0.000007892743],"category_scores_gemma":[0.0002133119,0.0001265456,0.0002227904,0.0003975073,0.0001466595,0.000003638214,0.0001369115,0.0001855545,0.000006412267],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000005129537,"about_ca_system_score_gemma":0.00003536361,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008496516,"about_ca_topic_score_gemma":0.00009521872,"domain_scores_codex":[0.9985385,0.0002996226,0.0003072511,0.0004924896,0.0001378333,0.0002243254],"domain_scores_gemma":[0.9991811,0.00004937971,0.00007804472,0.0005720254,0.0000334447,0.00008601444],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00004470404,0.0003572005,0.9724166,0.00001623684,0.0006529647,0.00001816028,0.0002289993,0.0002890764,0.009952579,0.0006721349,0.0009947929,0.01435654],"study_design_scores_gemma":[0.001167557,0.000830334,0.7327493,0.000006243115,0.0006686968,0.00001229863,0.0002682699,0.0001739016,0.00100031,0.002329771,0.2602165,0.0005768388],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9922574,0.0009790278,0.004548565,0.001275027,0.00001994746,0.000379467,0.00001938138,0.00001731357,0.0005039421],"genre_scores_gemma":[0.9957443,0.00201559,0.0005035925,0.001164685,0.00007636619,0.0002147334,0.0002377764,0.00001757951,0.00002534421],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2592217,"threshold_uncertainty_score":0.5160378,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03912780281512165,"score_gpt":0.3344778506418778,"score_spread":0.2953500478267562,"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."}}