{"id":"W2167445572","doi":"10.1142/s0219720014500279","title":"IncMD: Incremental trie-based structural motif discovery algorithm","year":2014,"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":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Structural motif; Motif (music); Scalability; Computer science; Nucleic acid secondary structure; Trie; Sequence motif; Computational biology; A priori and a posteriori; Algorithm; Data structure; Theoretical computer science; RNA; Biology; Genetics","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":[0.0009468371,0.000870297,0.001515682,0.003823051,0.000790983,0.001106406,0.003343614,0.001484986,0.004591474],"category_scores_gemma":[0.004402305,0.0007673154,0.001366699,0.002845926,0.0005357946,0.002061566,0.001723868,0.001686763,0.001753667],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008719907,"about_ca_system_score_gemma":0.002201334,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002819542,"about_ca_topic_score_gemma":0.004713781,"domain_scores_codex":[0.9989963,0.0001440031,0.0001081887,0.0002628227,0.0003814032,0.0001073351],"domain_scores_gemma":[0.9980927,0.000983466,0.0001666006,0.0002081452,0.0004445201,0.0001045338],"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":[0.000931176,0.0005894559,0.006306604,0.0004384042,0.0002754488,0.0005323082,0.0002770447,0.1203377,0.0171432,0.01289878,0.02223874,0.8180311],"study_design_scores_gemma":[0.0001489436,0.0001637257,0.0005107958,0.00001967053,0.00004119372,0.0004744067,0.00004565325,0.979371,0.004961581,0.008899606,0.005337632,0.00002577107],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02721694,0.0006240863,0.9613871,0.0003482671,0.00007781451,0.0003200918,0.001153109,0.007345651,0.001526984],"genre_scores_gemma":[0.08264842,0.0001593351,0.9110194,0.0002303241,0.00004277132,0.0004276625,0.003516587,0.0002095159,0.001745896],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004591474,"threshold_uncertainty_score":0.01536,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006031222069649855,"score_gpt":0.2317720276477768,"score_spread":0.2257408055781269,"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."}}