{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013269,0.001054173,0.001391163,0.001095479,0.0002875416,0.0007881442,0.0006945907,0.0007219827,0.001213048],"category_scores_gemma":[0.002935719,0.0005431688,0.001024638,0.0006008195,0.0003568548,0.0003989843,0.000427819,0.0006376031,0.0002242187],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006681081,"about_ca_system_score_gemma":0.0009729619,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003723105,"about_ca_topic_score_gemma":0.002681484,"domain_scores_codex":[0.9995492,0.0002259886,0.00003245871,0.00005262192,0.00009158094,0.00004806245],"domain_scores_gemma":[0.9978926,0.001735779,0.0001250395,0.0000469458,0.0001541391,0.00004551229],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008876772,0.00006869289,0.0009772219,0.00006506585,0.0000368121,0.0000577623,0.00001910005,0.9829226,0.003517801,0.0009112671,0.000124805,0.01121023],"study_design_scores_gemma":[0.000005970916,0.00005658002,0.0001309808,0.000004133587,0.00001144767,0.000009514846,0.000003445934,0.9978815,0.001437594,0.0003622375,0.0000927396,0.000003850432],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4485619,0.000757051,0.5426143,0.0003073245,0.00004498593,0.0002764928,0.000389527,0.002951143,0.004097275],"genre_scores_gemma":[0.8851944,0.0004166842,0.1122306,0.0001187805,0.00001726383,0.0003456209,0.000588588,0.00009268046,0.0009953304],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003723105,"threshold_uncertainty_score":0.007402897,"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."}}