{"id":"W2150717117","doi":"10.1093/bioinformatics/btr444","title":"Bayesian prediction of tissue-regulated splicing using RNA sequence and cellular context","year":2011,"lang":"en","type":"article","venue":"Bioinformatics","topic":"RNA Research and Splicing","field":"Biochemistry, Genetics and Molecular Biology","cited_by":116,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Canadian Institutes of Health Research","keywords":"RNA splicing; Source code; Context (archaeology); Alternative splicing; Feature (linguistics); Computational biology; Exon; Computer science; Inference; Gibbs sampling; Bayesian probability; Biology; Gene; RNA; Artificial intelligence; Genetics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001567256,0.00009067313,0.0001096071,0.00005066419,0.00006066185,0.00001207696,0.00008911297,0.00009569636,0.00001181979],"category_scores_gemma":[0.00003583379,0.0000828215,0.0000254266,0.00006847826,0.00009592299,0.0000139757,0.00006873281,0.00005214731,0.000001866187],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001005604,"about_ca_system_score_gemma":0.00004323751,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000141173,"about_ca_topic_score_gemma":0.000008137526,"domain_scores_codex":[0.9993429,0.00001600221,0.000262401,0.00009020975,0.0001108559,0.0001776335],"domain_scores_gemma":[0.9995357,0.000004154574,0.000105848,0.0001963044,0.00007400659,0.00008396545],"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":[0.00002485923,0.00001035189,0.001102363,0.00007811081,0.00002224272,0.000001320495,0.0003502507,0.00001338531,0.9901726,0.00005938504,0.00004055207,0.008124538],"study_design_scores_gemma":[0.0002772199,0.0002266564,0.0004706209,0.00004539622,0.00001311996,0.00001886203,0.0004066974,0.0571652,0.9410086,0.00002356705,0.0002590855,0.00008499965],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9593289,0.0001726988,0.03940741,0.000004714067,0.00003793403,0.0001504672,0.00002026804,0.000008839233,0.0008688036],"genre_scores_gemma":[0.9898258,0.00005153881,0.009874469,0.00002861556,0.00002887926,0.000001559147,0.00003116368,0.000009056776,0.0001488721],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05715181,"threshold_uncertainty_score":0.3377363,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03686261043592832,"score_gpt":0.2611324581602233,"score_spread":0.224269847724295,"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."}}