{"id":"W2127147859","doi":"10.1186/1471-2105-9-158","title":"Improving the prediction of mRNA extremities in the parasitic protozoan Leishmania","year":2008,"lang":"en","type":"article","venue":"BMC Bioinformatics","topic":"RNA Research and Splicing","field":"Biochemistry, Genetics and Molecular Biology","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Wilfrid Laurier University; Centre hospitalier de l'Université Laval; Centre hospitalier universitaire de Québec","funders":"Canadian Institutes of Health Research; Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Polyadenylation; RNA splicing; Biology; DNA microarray; Computational biology; splice; In silico; Genetics; Alternative splicing; Expressed sequence tag; Polypyrimidine tract; Untranslated region; Gene; Messenger RNA; RNA; Gene expression","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003533881,0.00007758355,0.00007653438,0.00003347895,0.000123126,0.00001910236,0.000259431,0.00006022574,0.000002763458],"category_scores_gemma":[0.0001863839,0.00004330726,0.00005028377,0.00009419184,0.0001391853,0.00001115864,0.00004136181,0.0001080758,0.000003108696],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00000907403,"about_ca_system_score_gemma":0.0001032422,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006722418,"about_ca_topic_score_gemma":0.00006385172,"domain_scores_codex":[0.9992283,0.00005008575,0.0002679668,0.00006493938,0.000201073,0.0001876286],"domain_scores_gemma":[0.9994958,0.0000424772,0.00009046544,0.0003028121,0.00004545156,0.00002301181],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004114245,0.0003831884,0.3939586,0.001480664,0.0001110207,0.00001395518,0.01352558,0.001693839,0.5602027,0.001124111,0.00989409,0.01720087],"study_design_scores_gemma":[0.003287604,0.001935493,0.2821245,0.0002113449,0.00004848771,0.0006566561,0.02107004,0.4081617,0.2732429,0.0001465373,0.008503102,0.0006116354],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9920779,0.0001207011,0.00561466,0.00004320987,0.00002950124,0.0005583499,0.00001246449,0.000005744295,0.001537447],"genre_scores_gemma":[0.9974217,0.00008882287,0.001985225,0.000100206,0.00007110939,0.00006678003,0.0000260264,0.000005965738,0.0002341426],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4064679,"threshold_uncertainty_score":0.1766019,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02669845954006209,"score_gpt":0.2490251544614015,"score_spread":0.2223266949213394,"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."}}