{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002017773,0.0006997935,0.0007432357,0.001349979,0.0002889861,0.0006825666,0.0008027855,0.0007835,0.001406847],"category_scores_gemma":[0.005440364,0.0004541174,0.0007216958,0.0008771126,0.0004677873,0.0006181393,0.0005284811,0.0009290875,0.0006816107],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007048391,"about_ca_system_score_gemma":0.0009026582,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006334361,"about_ca_topic_score_gemma":0.01112153,"domain_scores_codex":[0.9994388,0.0002077514,0.00003383829,0.000173654,0.000108759,0.00003715113],"domain_scores_gemma":[0.9971372,0.00210939,0.0002518431,0.0001440414,0.0002659851,0.00009146928],"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":[0.0009028896,0.000158259,0.07639172,0.0003533798,0.0002712872,0.0002706869,0.0001587895,0.6962564,0.04625419,0.006857411,0.003145851,0.1689791],"study_design_scores_gemma":[0.00002814647,0.00003084388,0.006896109,0.00001655897,0.00003396696,0.00009122508,0.00001097621,0.9773015,0.005580048,0.009405502,0.000589712,0.00001542827],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1912122,0.0008184161,0.8032004,0.0002352152,0.0000240703,0.0000605684,0.001819376,0.001358401,0.001271318],"genre_scores_gemma":[0.7767373,0.0004412151,0.2160222,0.0001258546,0.00006470909,0.0001174859,0.004981449,0.0002007997,0.001309033],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006334361,"threshold_uncertainty_score":0.012595,"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."}}