{"id":"W2060355037","doi":"10.1142/s0219720006002363","title":"RNALL: AN EFFICIENT ALGORITHM FOR PREDICTING RNA LOCAL SECONDARY STRUCTURAL LANDSCAPE IN GENOMES","year":2006,"lang":"en","type":"article","venue":"Journal of Bioinformatics and Computational Biology","topic":"RNA and protein synthesis mechanisms","field":"Biochemistry, Genetics and Molecular Biology","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; U.S. Department of Energy","keywords":"RNA; Computational biology; Nucleic acid secondary structure; Energy landscape; Computer science; Genome; Algorithm; Sliding window protocol; Bioinformatics; Data mining; Gene; Window (computing); Biology; Genetics","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":[0.001165645,0.002191168,0.001697797,0.003337522,0.001052473,0.001495279,0.003042547,0.001763943,0.008483919],"category_scores_gemma":[0.004347046,0.001155771,0.001666114,0.001975371,0.0006988794,0.002607547,0.001891086,0.00151859,0.003263051],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001208561,"about_ca_system_score_gemma":0.001408187,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00355376,"about_ca_topic_score_gemma":0.00563875,"domain_scores_codex":[0.9993494,0.0001699675,0.00004305885,0.000228063,0.0001382803,0.00007128091],"domain_scores_gemma":[0.9988585,0.0007626785,0.0001081751,0.00009552683,0.0001101713,0.00006512812],"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":[0.0008879495,0.0003764999,0.008641737,0.001244388,0.0003911677,0.0007261836,0.0004405779,0.3407298,0.02420685,0.01162765,0.03983512,0.5708921],"study_design_scores_gemma":[0.000118818,0.00005472115,0.0004913236,0.00003051831,0.00002970029,0.0001253301,0.00004781529,0.9815461,0.003188407,0.009284626,0.005056186,0.00002649081],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02628041,0.0005324812,0.9215327,0.0002807195,0.00005723573,0.0002341289,0.002475376,0.04699252,0.001614526],"genre_scores_gemma":[0.09732723,0.0002244847,0.890577,0.0002430689,0.00003834753,0.0008424622,0.006172794,0.003016396,0.001558313],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008483919,"threshold_uncertainty_score":0.02838153,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005349491978337566,"score_gpt":0.2317646768235121,"score_spread":0.2264151848451746,"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."}}