{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006948839,0.00016754,0.0001609158,0.00005312146,0.000171695,0.00002659795,0.0001032859,0.0002704664,0.000003810417],"category_scores_gemma":[0.00004926233,0.0001277977,0.00005320207,0.0000384153,0.00006174694,0.000005298108,0.00003393957,0.00002958053,8.022099e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000005587005,"about_ca_system_score_gemma":0.00002472437,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005616272,"about_ca_topic_score_gemma":0.000004591845,"domain_scores_codex":[0.9992262,0.00003142609,0.0003039838,0.0001817148,0.00004796937,0.0002087544],"domain_scores_gemma":[0.9995057,0.00002326125,0.0001646772,0.0001681109,0.00006525776,0.00007300703],"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":[0.0001770217,0.00003693229,0.0003087962,0.0000322987,0.0001007062,6.937768e-7,0.0001955962,0.00002237748,0.9045447,0.01513472,0.0009380133,0.07850818],"study_design_scores_gemma":[0.001612849,0.002237612,0.0007560779,0.000029848,0.00006673142,0.0000609055,0.0001587373,0.01617354,0.8940721,0.04241331,0.04183632,0.0005819796],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9135956,0.0009733468,0.08249307,0.0002742922,0.0004016387,0.0004866068,0.00006623059,0.00003735253,0.001671923],"genre_scores_gemma":[0.9793871,0.0002467774,0.0189733,0.0008385526,0.0001820319,0.00001665539,0.0002604435,0.000008582627,0.00008656711],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0779262,"threshold_uncertainty_score":0.521144,"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."}}