{"id":"W2085516557","doi":"10.1038/nsmb.1444","title":"Antisense transcripts are targets for activating small RNAs","year":2008,"lang":"en","type":"article","venue":"Nature Structural & Molecular Biology","topic":"RNA Research and Splicing","field":"Biochemistry, Genetics and Molecular Biology","cited_by":272,"is_retracted":false,"has_abstract":false,"ca_institutions":"Western University","funders":"National Institute of Biomedical Imaging and Bioengineering; National Institute of General Medical Sciences; U.S. Public Health Service","keywords":"Biology; Argonaute; Antisense RNA; RNA; Long non-coding RNA; Promoter; Gene expression; Gene; Molecular biology; Non-coding RNA; Small nucleolar RNA; Cell biology; Genetics; RNA interference","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001137379,0.0002946391,0.0002995486,0.00008159984,0.0002894906,0.00001622116,0.000331446,0.0007421225,0.000009979819],"category_scores_gemma":[0.0003959165,0.0002491895,0.0002554238,0.0001228617,0.0001831915,0.000004910421,0.00008370504,0.0004864123,0.000001787494],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002038046,"about_ca_system_score_gemma":0.00008525798,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002347767,"about_ca_topic_score_gemma":0.00002765734,"domain_scores_codex":[0.9982353,0.0001179653,0.0002390092,0.0006323553,0.0001182132,0.0006571477],"domain_scores_gemma":[0.9991186,0.00002245381,0.0001210077,0.0003627294,0.0002148954,0.0001603101],"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":[0.0002311053,0.000008972049,0.001563724,0.00002371059,0.00009800488,0.00005229347,0.00002138627,0.00001744429,0.9941968,0.0005732677,0.0005571392,0.002656176],"study_design_scores_gemma":[0.001129338,0.0004322757,0.006637731,0.00001242486,0.00001183085,0.0002625074,0.0000303792,0.0001433958,0.9767663,0.0005164835,0.01370227,0.0003550173],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9840093,0.004202803,0.01014652,0.0005968991,0.0003361811,0.0004562554,0.00008733309,0.00002924298,0.0001355201],"genre_scores_gemma":[0.9921415,0.0001006611,0.005400271,0.001147616,0.0003304784,0.00003222938,0.0004283687,0.00004438932,0.0003744586],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01743043,"threshold_uncertainty_score":0.999996,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01306207801450982,"score_gpt":0.3067119378618558,"score_spread":0.293649859847346,"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."}}