{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009276632,0.0004379049,0.0005564456,0.0001199676,0.00006741619,0.00001424144,0.0006690674,0.0006254181,0.0001067776],"category_scores_gemma":[0.0006319003,0.0004806205,0.0005451487,0.00008002284,0.0001264293,0.00000311618,0.001645526,0.0003221674,0.0000248104],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003443446,"about_ca_system_score_gemma":0.0001224284,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005268557,"about_ca_topic_score_gemma":0.000005587948,"domain_scores_codex":[0.9975116,0.0001313613,0.000613423,0.001123555,0.0001789412,0.0004410888],"domain_scores_gemma":[0.9974484,0.00004102753,0.0002966789,0.001697501,0.0003765628,0.0001398391],"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":[0.00009301752,0.00007961856,0.006615013,0.0007463478,0.0004007251,0.000001609117,0.000165069,0.002730022,0.9872165,0.00004029936,0.0001433181,0.001768455],"study_design_scores_gemma":[0.0004469907,0.0001041815,0.011548,0.0001275148,0.0001511673,0.000007481141,0.00004805774,0.0005148128,0.9554806,0.0004746609,0.03062701,0.0004695232],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6690282,0.0008953802,0.3278352,0.00004825234,0.0008206706,0.0006534784,0.00005181364,0.00003435876,0.0006326948],"genre_scores_gemma":[0.9325653,0.0003000513,0.06526131,0.00004232661,0.0008532189,0.0002612837,0.0001975212,0.00009287652,0.0004260603],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2635372,"threshold_uncertainty_score":0.9997646,"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."}}