{"id":"W2781785051","doi":"10.3390/biomedicines6010001","title":"Skipping Multiple Exons to Treat DMD—Promises and Challenges","year":2018,"lang":"en","type":"review","venue":"Biomedicines","topic":"CRISPR and Genetic Engineering","field":"Biochemistry, Genetics and Molecular Biology","cited_by":99,"is_retracted":false,"has_abstract":true,"ca_institutions":"Muscular Dystrophy Canada; University of Alberta","funders":"Faculty of Medicine and Dentistry, University of Alberta; University of Alberta; 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":"Exon skipping; Exon; Duchenne muscular dystrophy; Medicine; Bioinformatics; Muscular dystrophy; Computational biology; Pharmacology; Biology; Genetics; Gene; Internal medicine; 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":[0.0001151207,0.0003507482,0.0006325498,0.0001592251,0.00005321125,0.00001548416,0.0001755769,0.0002642561,0.000008298571],"category_scores_gemma":[0.000143083,0.0002655235,0.0001251185,0.000107805,0.00009397644,0.0000012023,0.0001627352,0.00005915774,0.00001896204],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000009931617,"about_ca_system_score_gemma":0.00004530238,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000090685,"about_ca_topic_score_gemma":0.00002128384,"domain_scores_codex":[0.9988233,0.00002779167,0.0002693816,0.0005260003,0.00009245286,0.0002610864],"domain_scores_gemma":[0.9992913,0.00003520251,0.00006228847,0.000393907,0.00004094883,0.000176379],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000005512328,0.0000175856,0.000004146093,0.007665765,0.00008244535,0.000004689123,0.00006585653,1.404468e-7,0.001639917,0.000002792174,0.003445115,0.987066],"study_design_scores_gemma":[0.0001887746,0.000284691,0.00001885364,0.002653499,0.000191537,0.00006016729,0.00004743866,0.000001159658,0.0005385557,0.000001462116,0.9957162,0.0002976322],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0002184309,0.9982238,0.0002888143,0.0001105294,0.000346945,0.0004047196,0.00003456457,0.0000250059,0.0003471618],"genre_scores_gemma":[0.0001554901,0.9965575,0.0005414361,0.00003746692,0.001798641,0.00008748715,0.0001229867,0.00005332406,0.0006456472],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9922711,"threshold_uncertainty_score":0.9999797,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06896496417085068,"score_gpt":0.3780366884314863,"score_spread":0.3090717242606356,"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."}}