{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001019225,0.0005417908,0.0006719208,0.0004156301,0.0003736365,0.0005661279,0.0002911429,0.0004090491,0.001343803],"category_scores_gemma":[0.0009806214,0.0004269172,0.0004003398,0.000198729,0.0004059105,0.0001531767,0.0003264775,0.0006345559,0.0003428441],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006889461,"about_ca_system_score_gemma":0.0005746512,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001673495,"about_ca_topic_score_gemma":0.004645547,"domain_scores_codex":[0.9989721,0.0002855024,0.000100679,0.000226234,0.0003222572,0.00009335466],"domain_scores_gemma":[0.9994992,0.0002326153,0.0001138298,0.00005158461,0.00007211063,0.00003073978],"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":[0.00005215434,0.00003658907,0.000121625,0.00002562456,0.000005474223,0.00001518241,0.00001657187,0.0007838988,0.9958091,0.000155344,0.00004603087,0.002932504],"study_design_scores_gemma":[0.00001949485,0.0002899929,0.000530936,0.000004548877,0.00002775659,0.00006300725,0.00001228173,0.004758664,0.9929076,0.00006362405,0.001308787,0.00001315506],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9143077,0.001032321,0.08015708,0.0002075146,0.0000720424,0.0003098991,0.0006059036,0.0007050571,0.002602513],"genre_scores_gemma":[0.9419315,0.0007478303,0.05223263,0.0001951666,0.00001979897,0.0001904148,0.0006190905,0.0001723778,0.003891181],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001673495,"threshold_uncertainty_score":0.005390227,"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."}}