{"id":"W2595978746","doi":"10.1101/106922","title":"Generally applicable transcriptome-wide analysis of translational efficiency using anota2seq","year":2017,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"RNA and protein synthesis mechanisms","field":"Biochemistry, Genetics and Molecular Biology","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Jewish General Hospital","funders":"Uppsala Multidisciplinary Center for Advanced Computational Science; Science for Life Laboratory; Knut och Alice Wallenbergs Stiftelse; Marshfield Clinic Research Foundation; Victorian Cancer Agency; Cancerfonden; Cancerföreningen i Stockholm; Vetenskapsrådet; U.S. Department of Health and Human Services","keywords":"Translational efficiency; Translational regulation; Translation (biology); Polysome; Ribosome profiling; Transcriptome; Biology; Computational biology; Gene expression; Messenger RNA; PI3K/AKT/mTOR pathway; DNA microarray; Cell biology; Gene; Ribosome; RNA; Genetics; Signal transduction","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":[0.00287057,0.001027011,0.001031645,0.0009477621,0.0006710174,0.001518039,0.000731707,0.0006613504,0.002561486],"category_scores_gemma":[0.004631134,0.0003555969,0.0008832289,0.0009902366,0.0005798758,0.0005823003,0.0009049601,0.001082602,0.001560257],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005340882,"about_ca_system_score_gemma":0.0007894551,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008889807,"about_ca_topic_score_gemma":0.001293254,"domain_scores_codex":[0.9983713,0.0004771418,0.0001302191,0.0005883257,0.0003467641,0.00008622748],"domain_scores_gemma":[0.9979803,0.0009382512,0.000242115,0.0003480945,0.0003839131,0.0001073597],"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":[0.003708102,0.0004141304,0.05472403,0.001567481,0.001191509,0.0004599968,0.0008281428,0.1075652,0.611535,0.01512952,0.02596066,0.1769162],"study_design_scores_gemma":[0.0001450436,0.000448102,0.0323239,0.00005826609,0.0001761134,0.0003861215,0.0002155982,0.7307712,0.1924755,0.01717264,0.02562387,0.0002036961],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2317446,0.0008579359,0.7261013,0.0002870882,0.000281457,0.0002732589,0.01884311,0.01765668,0.003954686],"genre_scores_gemma":[0.4225255,0.0002997414,0.5380574,0.0004949048,0.0001358706,0.000901958,0.03209279,0.003106532,0.002385367],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00287057,"threshold_uncertainty_score":0.01518124,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01843936128648606,"score_gpt":0.2404494717942813,"score_spread":0.2220101105077952,"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."}}