{"id":"W1620090002","doi":"10.4137/bbi.s2578","title":"Predicting Consensus Structures for RNA Alignments via Pseudo-Energy Minimization","year":2009,"lang":"en","type":"article","venue":"Bioinformatics and Biology Insights","topic":"RNA and protein synthesis mechanisms","field":"Biochemistry, Genetics and Molecular Biology","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"National Institute of Allergy and Infectious Diseases; American Heart Association; National Institutes of Health; Cancer Research Institute; National Science Foundation","keywords":"Multiple sequence alignment; Computer science; Sequence alignment; Sequence (biology); Energy minimization; Nucleic acid secondary structure; Heuristics; Protein secondary structure; Data mining; Set (abstract data type); Computational biology; Algorithm; Bioinformatics; RNA; Biology; Genetics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001242462,0.001037576,0.0009956834,0.001871682,0.0007161617,0.0008385953,0.001383957,0.000954342,0.003232282],"category_scores_gemma":[0.00667296,0.0006960857,0.001106632,0.001395821,0.0004966602,0.001587548,0.0007932255,0.001067387,0.002360531],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004879822,"about_ca_system_score_gemma":0.001074331,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006545943,"about_ca_topic_score_gemma":0.001690222,"domain_scores_codex":[0.9992965,0.0002357746,0.00005807781,0.0001650785,0.0002127208,0.00003163525],"domain_scores_gemma":[0.9976072,0.001386534,0.000281778,0.0003294209,0.0003341025,0.0000609804],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000517508,0.0002114868,0.007083368,0.0008414134,0.0002317371,0.0006166238,0.000241779,0.6043543,0.05255314,0.0372461,0.006797621,0.2893049],"study_design_scores_gemma":[0.00002221541,0.00004433624,0.0004723484,0.00001800781,0.00001631248,0.0001672071,0.00003227533,0.9675362,0.01150599,0.01820235,0.001965492,0.00001731407],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03917531,0.0002156728,0.9546313,0.00006380816,0.00003612665,0.00007687564,0.0005697779,0.004439621,0.0007913944],"genre_scores_gemma":[0.1486561,0.0002178576,0.8466031,0.00004256857,0.00001822232,0.0002361771,0.002519724,0.001083271,0.0006230611],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003232282,"threshold_uncertainty_score":0.010813,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01487018981729811,"score_gpt":0.2437949654931788,"score_spread":0.2289247756758807,"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."}}