{"id":"W2773386950","doi":"10.1016/j.ymeth.2017.12.006","title":"Determining mRNA half-lives on a transcriptome-wide scale","year":2017,"lang":"en","type":"article","venue":"Methods","topic":"RNA Research and Splicing","field":"Biochemistry, Genetics and Molecular Biology","cited_by":86,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hospital for Sick Children; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Transcriptome; Messenger RNA; Computational biology; Transcription (linguistics); RNA; Biology; Cell biology; Gene expression; Gene; Genetics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001008706,0.0002785717,0.0006266274,0.0006309701,0.0005454055,0.0008492056,0.000403696,0.0005333819,0.001823774],"category_scores_gemma":[0.001683745,0.0003336495,0.0005759537,0.0006912852,0.0004015153,0.0005237685,0.000334026,0.001311956,0.001072924],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005147149,"about_ca_system_score_gemma":0.0003402361,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006939407,"about_ca_topic_score_gemma":0.001843747,"domain_scores_codex":[0.9993864,0.00007256884,0.00003527506,0.0002711825,0.000193321,0.00004116772],"domain_scores_gemma":[0.9988639,0.0005408567,0.0001228961,0.0001721348,0.0002544781,0.00004571618],"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":[0.0001060598,0.00002388764,0.0025354,0.0001083793,0.00003307146,0.00001907524,0.00008829737,0.0009557812,0.9884406,0.000576663,0.0002536893,0.006858929],"study_design_scores_gemma":[0.00001594309,0.0002162136,0.04612124,0.00003083111,0.0001067839,0.0001626032,0.0001093841,0.01791241,0.9242975,0.001320239,0.009666482,0.00004036223],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.685605,0.003166117,0.284202,0.0003107257,0.000157919,0.0002471439,0.01817025,0.001606053,0.006534742],"genre_scores_gemma":[0.7604913,0.002981909,0.203732,0.0006147008,0.0001121132,0.001185859,0.02416289,0.0009004382,0.005818764],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.001823774,"threshold_uncertainty_score":0.006101191,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04785345151514285,"score_gpt":0.4103233000218433,"score_spread":0.3624698485067004,"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."}}