{"id":"W2768145059","doi":"10.1016/j.celrep.2017.11.006","title":"Control of Gene Expression in Senescence through Transcriptional Read-Through of Convergent Protein-Coding Genes","year":2017,"lang":"en","type":"article","venue":"Cell Reports","topic":"RNA regulation and disease","field":"Biochemistry, Genetics and Molecular Biology","cited_by":65,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Institute of Cancer Research; Institut National Du Cancer; Ligue Contre le Cancer; Fondation pour la Recherche Médicale; Institut National de la Santé et de la Recherche Médicale; Fondation ARC pour la Recherche sur le Cancer; Agence Nationale de la Recherche; Association pour la Recherche sur le Cancer","keywords":"Biology; Antisense RNA; Gene; Chromatin; Long non-coding RNA; RNA; Sense (electronics); Transcription (linguistics); Non-coding RNA; Gene expression; Genetics; Promoter; Regulation of gene expression; Senescence; Cell biology; Chemistry","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001218313,0.00009645573,0.0001629719,0.00001466699,0.00005784884,0.000008348054,0.00009789544,0.00009108628,0.00005227239],"category_scores_gemma":[0.00005649773,0.0000898086,0.00009557664,0.00001821594,0.0001070479,0.00001267064,0.00002560592,0.0000285333,4.81863e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00000683402,"about_ca_system_score_gemma":0.00009277142,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005194669,"about_ca_topic_score_gemma":0.000005132254,"domain_scores_codex":[0.9990578,0.00003096596,0.0003867651,0.0002510169,0.0001576307,0.0001157678],"domain_scores_gemma":[0.9988976,0.000003127226,0.000454515,0.0005036092,0.00009921773,0.00004190118],"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":[0.0001069213,0.0001015863,0.03692716,0.0000721672,0.000007761035,0.00003581952,0.00004892612,0.0001175983,0.9623787,0.00005514007,0.00004650393,0.0001017531],"study_design_scores_gemma":[0.0006171308,0.00003780842,0.02328785,0.00004983816,0.00001048169,0.00001172027,0.00002295624,0.00003560217,0.9735953,0.0001566841,0.002083039,0.00009162656],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9821472,0.001184718,0.01472778,0.00004337001,0.0001429681,0.0002786016,0.00001210541,0.000003461778,0.001459853],"genre_scores_gemma":[0.9975594,0.0001083508,0.001721509,0.00002339196,0.00005161613,0.00002016842,0.0000381129,0.00000924007,0.0004682431],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01541223,"threshold_uncertainty_score":0.3662288,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01749773234476903,"score_gpt":0.2601932127788475,"score_spread":0.2426954804340785,"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."}}