{"id":"W2148599336","doi":"10.1261/rna.7284905","title":"HotKnots: Heuristic prediction of RNA secondary structures including pseudoknots","year":2005,"lang":"en","type":"article","venue":"RNA","topic":"RNA and protein synthesis mechanisms","field":"Biochemistry, Genetics and Molecular Biology","cited_by":287,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Universiteit Leiden","keywords":"Pseudoknot; Nucleic acid secondary structure; Algorithm; Heuristic; R package; Dynamic programming; Matching (statistics); Software; Simple (philosophy); Computer science; Loop (graph theory); Biology; Protein secondary structure; Mathematics; RNA; Artificial intelligence; Combinatorics; Genetics; Gene; Computational science","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":[0.001214521,0.001110861,0.001521808,0.001546736,0.0009649878,0.001443759,0.001545738,0.001332797,0.003156085],"category_scores_gemma":[0.003148777,0.0008972979,0.001433873,0.001196937,0.0007645783,0.001363988,0.00097365,0.0009763815,0.001690438],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005645526,"about_ca_system_score_gemma":0.00130202,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001576583,"about_ca_topic_score_gemma":0.002501781,"domain_scores_codex":[0.999253,0.0002230652,0.00004445946,0.0001876406,0.0002143387,0.00007755641],"domain_scores_gemma":[0.9983875,0.0009011484,0.0001647157,0.000238272,0.0002160417,0.0000923625],"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":[0.001140076,0.0002718586,0.006302145,0.0007814614,0.0003161546,0.0007191371,0.0002807086,0.5425551,0.03249172,0.01973555,0.01423072,0.3811754],"study_design_scores_gemma":[0.00008443422,0.0001341809,0.0006113849,0.00003080099,0.0000387299,0.0002312683,0.00003749852,0.9648111,0.01385288,0.01555354,0.004574496,0.00003956503],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03608642,0.0006793246,0.9512085,0.00007672657,0.00009564451,0.0001388533,0.0004892239,0.009671303,0.001554118],"genre_scores_gemma":[0.2323173,0.0003729071,0.7609043,0.0001642168,0.00006654533,0.0002535641,0.002741709,0.001196359,0.00198314],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003156085,"threshold_uncertainty_score":0.01055819,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01547006879223549,"score_gpt":0.2464412099282184,"score_spread":0.2309711411359829,"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."}}