{"id":"W2159621058","doi":"10.1109/newcas.2010.5603764","title":"Prediction of secondary structure of RNAs with pseudoknots using matched filter","year":2010,"lang":"en","type":"article","venue":"","topic":"RNA and protein synthesis mechanisms","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Pseudoknot; Nucleic acid secondary structure; Protein secondary structure; RNA; Nucleic acid structure; Computer science; Computational biology; Base pair; Filter (signal processing); Algorithm; Theoretical computer science; Biology; Genetics; DNA; Gene","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":[0.0007183963,0.0004459505,0.0007103278,0.001316765,0.0005258053,0.0005814321,0.0004433224,0.001051949,0.0008093744],"category_scores_gemma":[0.001377512,0.0002862787,0.001017749,0.0006835665,0.00039097,0.0007822933,0.0003092368,0.0004313377,0.0004110686],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005308158,"about_ca_system_score_gemma":0.0007211441,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002221982,"about_ca_topic_score_gemma":0.002105393,"domain_scores_codex":[0.9996972,0.00005582036,0.00001747667,0.0000950621,0.0001006239,0.000033871],"domain_scores_gemma":[0.9990124,0.0005429282,0.0001861249,0.00007078303,0.0001372789,0.00005046021],"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":[0.0006692304,0.0002504587,0.01280999,0.0002505775,0.0001529719,0.0007696976,0.0001934006,0.4197634,0.3214617,0.01396557,0.0009019748,0.228811],"study_design_scores_gemma":[0.000008453131,0.00005017778,0.001416477,0.000003619245,0.00001098975,0.00009779482,0.00001031213,0.9751424,0.01995723,0.002960792,0.000329356,0.00001239049],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08556357,0.00009780194,0.913348,0.0000289113,0.00001175973,0.00002377893,0.00007870645,0.000517469,0.0003300078],"genre_scores_gemma":[0.5091382,0.0001515578,0.4889067,0.00003753193,0.0000222461,0.00006065893,0.0005636831,0.00008118254,0.001038276],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002221982,"threshold_uncertainty_score":0.004418135,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01048248537611007,"score_gpt":0.2140138895578496,"score_spread":0.2035314041817395,"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."}}