{"id":"W2899187507","doi":"10.20944/preprints201811.0018.v1","title":"Methods of CRISPR/Cas9 Exon Skipping for Duchenne Muscular Dystrophy","year":2018,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"CRISPR and Genetic Engineering","field":"Biochemistry, Genetics and Molecular Biology","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Muscular Dystrophy Canada; University of Alberta","funders":"Faculty of Medicine and Dentistry, University of Alberta; Alberta Innovates; University of Alberta; Japan Society for the Promotion of Science; Women and Children's Health Research Institute; Children's Health Research Institute; Muscular Dystrophy Canada; Canadian Institutes of Health Research; Parent Project Muscular Dystrophy","keywords":"CRISPR; Genome editing; Duchenne muscular dystrophy; Exon skipping; Dystrophin; Cas9; Genetic enhancement; Muscular dystrophy; Medicine; Biology; Genetics; Computational biology; Bioinformatics; Exon; 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":[0.0007272963,0.0008215906,0.0008287544,0.0008442996,0.0006202186,0.00085021,0.0008547548,0.00107013,0.004023907],"category_scores_gemma":[0.0006671437,0.0005508277,0.0007694608,0.000497506,0.0004536376,0.0005630232,0.0008615227,0.001580312,0.002917626],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004478165,"about_ca_system_score_gemma":0.0007954055,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009794487,"about_ca_topic_score_gemma":0.001311643,"domain_scores_codex":[0.9988456,0.0001407361,0.0001599433,0.0002916729,0.0004792661,0.00008286659],"domain_scores_gemma":[0.9998164,0.00004932514,0.00003543444,0.00003033899,0.00004616368,0.00002253886],"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":[0.0001810407,0.000147945,0.0004140422,0.003244237,0.00009775287,0.0009149814,0.0002545343,0.001946325,0.7940602,0.008815622,0.01027514,0.1796481],"study_design_scores_gemma":[0.00008026142,0.0006180886,0.001523936,0.0003633477,0.0001535008,0.002935106,0.00009635992,0.006357539,0.6342406,0.002389179,0.3510323,0.0002097269],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0678721,0.1093158,0.7485214,0.002040978,0.002567703,0.002967807,0.004199084,0.008188074,0.05432704],"genre_scores_gemma":[0.3408176,0.1022725,0.4977133,0.002092279,0.0002848252,0.004288153,0.006092788,0.001077364,0.04536125],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004023907,"threshold_uncertainty_score":0.01346129,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08335447276990914,"score_gpt":0.4411553610452081,"score_spread":0.357800888275299,"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."}}