{"id":"W2082317627","doi":"10.1016/s0304-3975(01)00192-x","title":"Computing similarity between RNA structures","year":2002,"lang":"en","type":"article","venue":"Theoretical Computer Science","topic":"RNA and protein synthesis mechanisms","field":"Biochemistry, Genetics and Molecular Biology","cited_by":60,"is_retracted":false,"has_abstract":false,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"RNA; Combinatorics; Nucleic acid secondary structure; Protein tertiary structure; Similarity (geometry); Mathematics; Protein secondary structure; Maximization; Algorithm; Base pair; Time complexity; Nucleic acid structure; Set (abstract data type); Computer science; Chemistry; Biology; Genetics; DNA; Artificial intelligence; Mathematical optimization","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001103482,0.0005555435,0.001569474,0.005955228,0.001011179,0.002176916,0.00185738,0.002064861,0.005543815],"category_scores_gemma":[0.01363192,0.0004897742,0.001410999,0.003858969,0.00106136,0.004879553,0.001872556,0.001080034,0.0008710294],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00112971,"about_ca_system_score_gemma":0.000704642,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001292697,"about_ca_topic_score_gemma":0.001930932,"domain_scores_codex":[0.9980633,0.0003578765,0.0001570952,0.0006348054,0.0006312839,0.0001557117],"domain_scores_gemma":[0.9934975,0.003979158,0.0005439803,0.00116188,0.000515917,0.0003016521],"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":[0.003590052,0.0008823557,0.0453292,0.0009695206,0.0008264284,0.001024817,0.0006207101,0.1873463,0.03549539,0.08610056,0.009209615,0.6286051],"study_design_scores_gemma":[0.0000934761,0.0003846716,0.005150313,0.00003300612,0.0001337847,0.0005658245,0.0002313326,0.856149,0.009617214,0.1256197,0.001986076,0.00003571083],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.7714613,0.001470402,0.2172258,0.0005902629,0.0001262272,0.0001308734,0.0011272,0.002168118,0.005699812],"genre_scores_gemma":[0.9384174,0.0002472669,0.05799256,0.00007475566,0.0001025836,0.00005402997,0.002002393,0.0001253441,0.0009836305],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.005955228,"threshold_uncertainty_score":0.01854593,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01658480587679013,"score_gpt":0.2512811595752036,"score_spread":0.2346963536984135,"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."}}