{"id":"W4382047291","doi":"10.20944/preprints202306.1818.v1","title":"HCS-Splice: A High-Content Screening Method to Advance the Discovery of RNA Splicing-Modulating Therapeutics","year":2023,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"RNA Research and Splicing","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"George Brown College","funders":"Università degli Studi di Trento","keywords":"RNA splicing; Exon; Alternative splicing; splice; Exon skipping; Minigene; Computational biology; Biology; RNA; Genetics; Gene; Cell biology","routes":{"ca_aff":true,"ca_fund":false,"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":[0.001658932,0.0009071684,0.001028214,0.001523892,0.0003139225,0.0009128459,0.000687456,0.00095237,0.002827696],"category_scores_gemma":[0.001123431,0.0004937956,0.0007428809,0.0006347951,0.0006088582,0.0005699011,0.0008959763,0.001324563,0.002183279],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004402729,"about_ca_system_score_gemma":0.0004959674,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003141733,"about_ca_topic_score_gemma":0.0005486704,"domain_scores_codex":[0.9981676,0.0003755999,0.0000955084,0.0002702062,0.0009741216,0.000116753],"domain_scores_gemma":[0.9992716,0.0002674803,0.0001056393,0.0001281941,0.000144396,0.00008267392],"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":[0.0001067202,0.00008306132,0.0002943867,0.0001121907,0.0000287769,0.0001208952,0.00003629514,0.0005028805,0.9770921,0.001264007,0.0009556753,0.019403],"study_design_scores_gemma":[0.00003179476,0.0002284854,0.000856766,0.00001209503,0.0000296571,0.0004378278,0.00001537694,0.007631453,0.9822137,0.000713003,0.007797403,0.00003242421],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1847862,0.003657412,0.7875346,0.0009470185,0.0003790208,0.000960457,0.002874121,0.007031844,0.01182932],"genre_scores_gemma":[0.4964256,0.003829317,0.4756438,0.00110451,0.0002164613,0.001227881,0.004618054,0.0009286421,0.01600581],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002827696,"threshold_uncertainty_score":0.009459615,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1878182262133575,"score_gpt":0.4134426924414121,"score_spread":0.2256244662280546,"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."}}