{"id":"W2932166412","doi":"10.1093/nar/gkz223","title":"Generally applicable transcriptome-wide analysis of translation using anota2seq","year":2019,"lang":"en","type":"article","venue":"Nucleic Acids Research","topic":"RNA and protein synthesis mechanisms","field":"Biochemistry, Genetics and Molecular Biology","cited_by":107,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Jewish General Hospital","funders":"National Cancer Institute; National Health and Medical Research Council; Fonds de Recherche du Québec - Santé; Canadian Institutes of Health Research; U.S. Department of Health and Human Services; National Institutes of Health; Cancerfonden; Cancerföreningen i Stockholm; Vetenskapsrådet; Medical Research Council; Marshfield Clinic Research Foundation; Victorian Cancer Agency","keywords":"Biology; Transcriptome; Translation (biology); Computational biology; Genetics; Evolutionary biology; Gene; Gene expression; Messenger RNA","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":[0.0009673439,0.0001139521,0.0002539257,0.0003068948,0.00008095981,0.00002643195,0.0003151473,0.0001983259,0.0004533689],"category_scores_gemma":[0.00003400982,0.0001101161,0.000202987,0.0008011367,0.00007677996,0.000007479251,0.00004388275,0.0001205297,0.00002651999],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001943556,"about_ca_system_score_gemma":0.00007780774,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008302002,"about_ca_topic_score_gemma":0.00001777944,"domain_scores_codex":[0.9983271,0.0002129143,0.0002458013,0.0003864222,0.000474776,0.0003529678],"domain_scores_gemma":[0.9991345,0.00003963631,0.00004757174,0.0005164224,0.0001839375,0.00007787521],"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":[0.0001196945,0.0000426072,0.007379734,0.00003053281,0.0002861952,6.877638e-7,0.00007659706,0.0007118445,0.9861334,0.0001039665,0.00005576462,0.005058975],"study_design_scores_gemma":[0.0003865642,0.0002512747,0.003470679,0.00001298843,0.000113068,0.000001109758,0.0001009944,0.004771255,0.9789308,0.0001595368,0.01163471,0.000167015],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9835065,0.0005115419,0.01260217,0.0000671585,0.0000355518,0.0003711756,0.00002061209,0.000008020905,0.002877256],"genre_scores_gemma":[0.9932876,0.0001178245,0.005702436,0.00004650022,0.0000534147,0.00001880149,0.00005218893,0.00002644791,0.0006948359],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01157894,"threshold_uncertainty_score":0.4964072,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05442791590393418,"score_gpt":0.3330112628059435,"score_spread":0.2785833469020093,"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."}}