{"id":"W2741955929","doi":"10.1016/j.ymthe.2017.07.014","title":"Quantitative Antisense Screening and Optimization for Exon 51 Skipping in Duchenne Muscular Dystrophy","year":2017,"lang":"en","type":"article","venue":"Molecular Therapy","topic":"Muscle Physiology and Disorders","field":"Biochemistry, Genetics and Molecular Biology","cited_by":87,"is_retracted":false,"has_abstract":false,"ca_institutions":"Muscular Dystrophy Canada; University of Alberta","funders":"Canadian Institutes of Health Research; National Institutes of Health; Muscular Dystrophy Canada; Canada Foundation for Innovation; University of Alberta; Faculty of Medicine and Dentistry, University of Alberta; Association Française contre les Myopathies; Alberta Innovates - Health Solutions; Japan Society for the Promotion of Science; Women and Children's Health Research Institute; National Institute of Arthritis and Musculoskeletal and Skin Diseases; Medical Research Council; Eunice Kennedy Shriver National Institute of Child Health and Human Development; U.S. Department of Energy","keywords":"Duchenne muscular dystrophy; Exon skipping; Exon; Muscular dystrophy; Genetics; Biology; Medicine; Gene; Alternative splicing","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0001518031,0.0001425563,0.0001494904,0.00004585658,0.0002227108,0.00004813271,0.0001422565,0.0001264951,0.000004975897],"category_scores_gemma":[0.0001114676,0.0001451221,0.00007744853,0.00002702358,0.0001196426,0.00001092827,0.00005003281,0.00005805421,5.318417e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000003656524,"about_ca_system_score_gemma":0.0000163757,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004051524,"about_ca_topic_score_gemma":0.00001280488,"domain_scores_codex":[0.9992021,0.00006405256,0.0001302947,0.0003468381,0.00005263509,0.0002041308],"domain_scores_gemma":[0.9994344,0.00001146033,0.0001016736,0.0003557254,0.00005643178,0.00004036024],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0007472337,0.0000517089,0.0007234814,0.00001588554,0.00008006045,0.000008748364,0.00008016978,0.01081414,0.9779089,0.0005296717,0.00001629099,0.009023725],"study_design_scores_gemma":[0.01940608,0.003779095,0.04706018,0.000166323,0.00006206543,0.00002086478,0.0006421701,0.07366794,0.8437182,0.003008387,0.006816762,0.001651989],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8142688,0.001426124,0.1837002,0.0001872949,0.0000454109,0.0002850235,0.000006260786,0.000006232648,0.0000746673],"genre_scores_gemma":[0.9770797,0.0006780922,0.02177079,0.0002647839,0.0000327113,0.00004770691,0.00008466473,0.00002501223,0.00001656214],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1628109,"threshold_uncertainty_score":0.5917906,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02423810728320082,"score_gpt":0.3033948778584242,"score_spread":0.2791567705752234,"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."}}