{"id":"W2024664316","doi":"10.1371/journal.pone.0120058","title":"In Silico Screening Based on Predictive Algorithms as a Design Tool for Exon Skipping Oligonucleotides in Duchenne Muscular Dystrophy","year":2015,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Muscle Physiology and Disorders","field":"Biochemistry, Genetics and Molecular Biology","cited_by":61,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Faculty of Medicine and Dentistry, University of Alberta; Muscular Dystrophy Canada; University of Alberta; Duchenne Parent Project; Women and Children's Health Research Institute; Children's Health Research Institute; French Muscular Dystrophy Association; Canadian Institutes of Health Research; Parent Project Muscular Dystrophy; Muscular Dystrophy Association","keywords":"Exon skipping; Duchenne muscular dystrophy; In silico; Oligonucleotide; Morpholino; Exon; Computational biology; RNA splicing; Bioinformatics; Biology; Dystrophin; Genetics; Alternative splicing; RNA; DNA; Gene","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":[0.0003431545,0.0001686013,0.0002277408,0.0001091565,0.00003972695,0.00001171734,0.0001397078,0.0001729868,0.000007724355],"category_scores_gemma":[0.0005720624,0.0001772605,0.00006438563,0.0001195888,0.0000586281,0.00001058561,0.00004257877,0.0001245292,0.000005900689],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002799251,"about_ca_system_score_gemma":0.00009274279,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006064019,"about_ca_topic_score_gemma":0.00001264229,"domain_scores_codex":[0.9988008,0.0001358601,0.0002051683,0.0004123097,0.0001361765,0.0003097473],"domain_scores_gemma":[0.9994653,0.00006088542,0.00005912449,0.0002558541,0.00008193546,0.00007685308],"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":[0.005240345,0.002659882,0.00402148,0.0001173859,0.0002114301,0.00002061737,0.0002903832,0.04216437,0.9436482,0.0000720271,0.0002749712,0.00127894],"study_design_scores_gemma":[0.01131928,0.006870851,0.01103315,0.000482562,0.0001090294,0.000001852996,0.0005789669,0.245088,0.7212415,0.002180738,0.000240915,0.0008532334],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9788734,0.0001601129,0.01982219,0.0002053344,0.00002892507,0.000710973,0.00001458469,0.00001546446,0.0001690224],"genre_scores_gemma":[0.9838523,0.00001741824,0.01498325,0.0005985785,0.0001248787,0.0002815258,0.00008150184,0.00002651577,0.0000340581],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2224067,"threshold_uncertainty_score":0.7228475,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04937945172095676,"score_gpt":0.2545366683126604,"score_spread":0.2051572165917036,"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."}}