{"id":"W2128220972","doi":"10.1101/gr.162230.113","title":"Identifying mRNA sequence elements for target recognition by human Argonaute proteins","year":2014,"lang":"en","type":"article","venue":"Genome Research","topic":"RNA Research and Splicing","field":"Biochemistry, Genetics and Molecular Biology","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Argonaute; Biology; RNA; microRNA; RNA-binding protein; Gene silencing; Computational biology; Messenger RNA; RNA-induced silencing complex; Genetics; RNA silencing; RNA interference; Gene","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":[0.002658507,0.0001181332,0.0001208331,0.0001255556,0.0005201869,0.0001187269,0.0003908292,0.0001118788,0.00006597362],"category_scores_gemma":[0.0003643027,0.0001185623,0.00007165266,0.0001601952,0.0001115584,0.000009986682,0.0002098567,0.0002301415,0.00008106406],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005744126,"about_ca_system_score_gemma":0.00008548311,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007634438,"about_ca_topic_score_gemma":0.00002610189,"domain_scores_codex":[0.9977537,0.0002415377,0.0002280883,0.0004843904,0.0004809348,0.0008113508],"domain_scores_gemma":[0.9990001,0.00003012357,0.0000487932,0.0003609639,0.0003643726,0.0001956417],"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":[0.00005478706,0.00004121571,0.0002505441,0.00008095978,0.00002870896,0.000001147367,0.00001836009,0.000002010846,0.9911805,0.00006969026,0.002659835,0.00561218],"study_design_scores_gemma":[0.001012231,0.001145237,0.0004824095,0.00003668468,0.000004022504,0.000004405419,0.00008146078,0.0003436464,0.8799716,0.003412988,0.1132351,0.0002701886],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.976773,0.0004050742,0.01979738,0.0002282349,0.0000347018,0.001174664,0.0001371882,0.000015786,0.001433939],"genre_scores_gemma":[0.9764655,0.0001767385,0.007339397,0.00008370038,0.0004746505,0.0006654974,0.001760426,0.00004930453,0.0129848],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.111209,"threshold_uncertainty_score":0.483483,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1161097859879423,"score_gpt":0.4046512644890861,"score_spread":0.2885414785011438,"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."}}