{"id":"W4407107404","doi":"10.3390/genes16020185","title":"Integrating Machine Learning-Based Approaches into the Design of ASO Therapies","year":2025,"lang":"en","type":"review","venue":"Genes","topic":"CRISPR and Genetic Engineering","field":"Biochemistry, Genetics and Molecular Biology","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Muscular Dystrophy Canada; University of Alberta","funders":"Faculty of Medicine and Dentistry, University of Alberta; Canadian Institutes of Health Research; Muscular Dystrophy Canada; University of Alberta; Alberta Innovates - Health Solutions; Women and Children's Health Research Institute; Children's Health Research Institute; Alberta Innovates; Heart and Stroke Foundation of Canada; U.S. Department of Defense","keywords":"Generalizability theory; Computer science; Machine learning; Clinical trial; Medicine; Exon skipping; Risk analysis (engineering); Artificial intelligence; Bioinformatics; Biology; RNA","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":[0.0009940451,0.001097354,0.001516932,0.001711092,0.00025053,0.001335147,0.001207157,0.001485505,0.003478649],"category_scores_gemma":[0.001123994,0.0004723141,0.000855526,0.001111888,0.0006042836,0.001594479,0.0007737055,0.003280254,0.002067118],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008872941,"about_ca_system_score_gemma":0.0008861371,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007488932,"about_ca_topic_score_gemma":0.001079896,"domain_scores_codex":[0.9996722,0.00006805102,0.00003473505,0.0000646484,0.000126547,0.00003385659],"domain_scores_gemma":[0.9995925,0.0002455265,0.00004335681,0.00001661979,0.00007811568,0.0000238486],"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":[0.0000628992,0.0001301748,0.0001394165,0.01360349,0.0001441006,0.0002553508,0.00006902343,0.003758505,0.005069187,0.02424577,0.01650207,0.93602],"study_design_scores_gemma":[0.00003954049,0.0002024489,0.0002852631,0.002519946,0.0001144913,0.0006252087,0.00004118872,0.001431824,0.002869002,0.0120006,0.9798274,0.00004308118],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0004256601,0.9901008,0.004586556,0.0007748495,0.0004283319,0.00002958154,0.0000416336,0.00004651652,0.00356609],"genre_scores_gemma":[0.00348496,0.9904372,0.003831127,0.0004513923,0.0002609307,0.00004466689,0.00007116428,0.00001056547,0.001407959],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.003478649,"threshold_uncertainty_score":0.01163721,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03778373683802093,"score_gpt":0.3230982357399931,"score_spread":0.2853144989019722,"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."}}