{"id":"W2614469195","doi":"10.1093/nar/gkx429","title":"RNA-MoIP: prediction of RNA secondary structure and local 3D motifs from sequence data","year":2017,"lang":"en","type":"article","venue":"Nucleic Acids Research","topic":"RNA and protein synthesis mechanisms","field":"Biochemistry, Genetics and Molecular Biology","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Institute for Research in Immunology and Cancer; McGill University","funders":"National Institute of General Medical Sciences; Canadian Institutes of Health Research","keywords":"Biology; RNA; Sequence (biology); Computational biology; Nucleic acid structure; Genetics; Nucleic acid secondary structure; Base sequence; Evolutionary biology; DNA; Gene","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.001043238,0.001395996,0.001157529,0.00144588,0.000551687,0.001040705,0.001724877,0.0009409058,0.005748031],"category_scores_gemma":[0.002860438,0.0008234496,0.001272932,0.001105412,0.0003597303,0.0009315769,0.0008655491,0.001192773,0.002562193],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005879306,"about_ca_system_score_gemma":0.001102923,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002390977,"about_ca_topic_score_gemma":0.003759759,"domain_scores_codex":[0.9996021,0.00007825848,0.00002824561,0.0001235461,0.0001377744,0.0000300862],"domain_scores_gemma":[0.9995323,0.0002309136,0.00005812731,0.00006313915,0.00005463837,0.00006077206],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002522255,0.0005131515,0.02631759,0.00254879,0.0009125269,0.002014241,0.0005160533,0.1601398,0.3392754,0.01622689,0.0502451,0.3987682],"study_design_scores_gemma":[0.0001140084,0.0001512177,0.004220256,0.00003248308,0.00006161331,0.0006781728,0.00005407393,0.9339488,0.04808696,0.003847385,0.00874653,0.00005862688],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0843396,0.0004003649,0.8129163,0.0001609066,0.00007363385,0.0002189935,0.009260396,0.09090898,0.001720933],"genre_scores_gemma":[0.2220464,0.000326415,0.7432601,0.0001194832,0.00003915592,0.0003918575,0.02769366,0.004775776,0.001346994],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005748031,"threshold_uncertainty_score":0.01922911,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0670910663677438,"score_gpt":0.3377760139636556,"score_spread":0.2706849475959118,"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."}}