{"id":"W2159507425","doi":"10.1109/tcbb.2008.88","title":"Improving Strand Pairing Prediction through Exploring Folding Cooperativity","year":2008,"lang":"en","type":"article","venue":"IEEE/ACM Transactions on Computational Biology and Bioinformatics","topic":"Protein Structure and Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"U.S. National Library of Medicine; National Institutes of Health; University of California, Riverside; Uniwersytet Warszawski; University of British Columbia; Pennsylvania State University","keywords":"Pairing; Folding (DSP implementation); Computer science; Consistency (knowledge bases); Hydrogen bond; Cooperativity; Algorithm; Topology (electrical circuits); Beta sheet; Theoretical computer science; Mathematics; Chemistry; Physics; Combinatorics; Molecule; Artificial intelligence; Protein structure; Engineering","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001835633,0.0006666559,0.001336128,0.001106413,0.0004429122,0.0008376474,0.0009434681,0.000810312,0.0009043408],"category_scores_gemma":[0.003800405,0.0004080602,0.0004118508,0.0007695148,0.0003815104,0.001370295,0.00083215,0.0005433016,0.0003177962],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004235901,"about_ca_system_score_gemma":0.0007797237,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001214045,"about_ca_topic_score_gemma":0.001486079,"domain_scores_codex":[0.9992525,0.0003124598,0.00004335554,0.0001703958,0.0001504636,0.00007080187],"domain_scores_gemma":[0.9974177,0.001562509,0.0002809738,0.0003460743,0.0002870323,0.0001057013],"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":[0.000586561,0.0003316919,0.01803482,0.00008240504,0.0001157314,0.0001558106,0.0001141564,0.741921,0.0185279,0.002370659,0.0008457179,0.2169135],"study_design_scores_gemma":[0.000008835655,0.00003309412,0.0003921053,0.000002465791,0.00001034028,0.00001885525,0.000009031145,0.9954619,0.002771917,0.00117735,0.0001107553,0.000003312252],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6630742,0.0002792667,0.3323395,0.0001451246,0.00001557214,0.00005959775,0.00007397393,0.00156752,0.002445248],"genre_scores_gemma":[0.9172376,0.00007903311,0.08185175,0.00003982654,0.00001225714,0.00003642372,0.0001752777,0.00008289252,0.0004850571],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001835633,"threshold_uncertainty_score":0.009707868,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03134504436585141,"score_gpt":0.2561702239180086,"score_spread":0.2248251795521572,"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."}}