{"id":"W2103848900","doi":"10.1093/nar/gks241","title":"R- chie : a web server and R package for visualizing RNA secondary structures","year":2012,"lang":"en","type":"article","venue":"Nucleic Acids Research","topic":"RNA and protein synthesis mechanisms","field":"Biochemistry, Genetics and Molecular Biology","cited_by":143,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Canadian Institutes of Health Research; Natural Sciences and Engineering Research Council of Canada; University of British Columbia; Michael Smith Health Research BC","keywords":"RNA; Visualization; Web server; Nucleic acid secondary structure; Protein secondary structure; Biology; Nucleic acid structure; Computational biology; Sequence (biology); Computer science; Structural motif; Graph; Software; Algorithm; Theoretical computer science; Data mining; The Internet; World Wide Web; Genetics; Programming language","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":[0.001154053,0.0001351795,0.0001365063,0.00007786094,0.0002735315,0.00006537967,0.0002225689,0.000213265,0.000208324],"category_scores_gemma":[0.0002293339,0.0001177257,0.00006729325,0.0000865427,0.0001208538,0.0000130866,0.0002448492,0.00017706,0.00001800831],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001372766,"about_ca_system_score_gemma":0.00007476013,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009090038,"about_ca_topic_score_gemma":0.000006877097,"domain_scores_codex":[0.9985285,0.0001774434,0.0001388798,0.0002976975,0.0002554618,0.0006020193],"domain_scores_gemma":[0.9992722,0.00005616675,0.00003144993,0.0003468615,0.00009963629,0.0001937419],"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":[0.0001057027,0.00002422542,0.0007740791,0.00006823593,0.0000363803,8.411116e-7,0.0001207697,6.615811e-8,0.9708171,0.002251078,0.002724516,0.02307698],"study_design_scores_gemma":[0.0004879058,0.0002973572,0.003047612,0.0000142098,0.000007271544,0.00001307104,0.0002490139,0.00001166248,0.8780692,0.00175169,0.1158746,0.0001764617],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9933932,0.002561178,0.0004760058,0.0001993245,0.0001109261,0.0004233954,0.00003903467,0.00001393403,0.002782962],"genre_scores_gemma":[0.9956535,0.0002213989,0.002254807,0.0001402012,0.0005924678,0.00007868376,0.00003360608,0.0000442602,0.0009810845],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1131501,"threshold_uncertainty_score":0.4800716,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0428296749839907,"score_gpt":0.3480202100672741,"score_spread":0.3051905350832834,"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."}}