{"id":"W2325808437","doi":"","title":"Machine learning in computational biology: models of alternative splicing","year":2009,"lang":"en","type":"dissertation","venue":"TSpace","topic":"RNA Research and Splicing","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computational biology; RNA splicing; Alternative splicing; Computer science; Markov chain Monte Carlo; DNA microarray; Probabilistic logic; Bayesian probability; Biology; Machine learning; Data mining; Artificial intelligence; Gene; Genetics; Exon; Gene expression; RNA","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":[0.003153575,0.001015155,0.001519505,0.0007747522,0.0004676422,0.002194667,0.001870745,0.002297572,0.002965888],"category_scores_gemma":[0.01188611,0.0006710354,0.001293237,0.001905412,0.002133444,0.003217672,0.001792359,0.004234536,0.0009338947],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001204401,"about_ca_system_score_gemma":0.001082814,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003101312,"about_ca_topic_score_gemma":0.002229341,"domain_scores_codex":[0.9984102,0.001034291,0.00005225378,0.0001837191,0.0002477313,0.0000718382],"domain_scores_gemma":[0.990649,0.008236444,0.0001926614,0.0003809123,0.0003889882,0.0001519539],"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":[0.00004720615,0.00005351842,0.0009881933,0.000330106,0.0001738058,0.00007907941,0.0001774075,0.4823556,0.0002751218,0.4505614,0.01365658,0.05130189],"study_design_scores_gemma":[0.00001394906,0.00001015416,0.0001075744,0.00002646266,0.000008728698,0.00001810129,0.00001039468,0.7086844,0.0000735191,0.2859418,0.005096314,0.00000858374],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006647793,0.0119038,0.9652915,0.007255897,0.0004211698,0.00003334081,0.0002336479,0.0002812488,0.007931694],"genre_scores_gemma":[0.4768803,0.02794599,0.4635245,0.003284457,0.003783414,0.001002206,0.001318798,0.0004854929,0.02177488],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003153575,"threshold_uncertainty_score":0.01667792,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01893258739830527,"score_gpt":0.3557763059680614,"score_spread":0.3368437185697561,"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."}}