{"id":"W2395761288","doi":"10.1186/s12859-016-1074-x","title":"RNA motif search with data-driven element ordering","year":2016,"lang":"en","type":"article","venue":"BMC Bioinformatics","topic":"RNA and protein synthesis mechanisms","field":"Biochemistry, Genetics and Molecular Biology","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"National Institute of General Medical Sciences; Division of Molecular and Cellular Biosciences; Vedecká Grantová Agentúra MŠVVaŠ SR a SAV; Eurostars; Agentúra na Podporu Výskumu a Vývoja; Pew Charitable Trusts; National Institutes of Health; National Science Foundation","keywords":"Speedup; RNA; Motif (music); Computer science; Computational biology; Backtracking; Search algorithm; Structural motif; Nucleic acid structure; Algorithm; Biology; Genetics; Gene; Physics","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":[0.0006535723,0.0006182365,0.0008038545,0.0006852875,0.0002939294,0.0006734129,0.001500774,0.0009679663,0.002994957],"category_scores_gemma":[0.002481033,0.0003461524,0.0005908681,0.00100833,0.0004346133,0.0008919958,0.001155656,0.000862335,0.001027007],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004308646,"about_ca_system_score_gemma":0.0008691409,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000955925,"about_ca_topic_score_gemma":0.001609683,"domain_scores_codex":[0.9993766,0.000181165,0.00004120242,0.0001676038,0.0001645208,0.00006889955],"domain_scores_gemma":[0.9987378,0.0008012021,0.00008926835,0.0001505914,0.0001539703,0.00006724725],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001853579,0.0004849157,0.005470115,0.001014333,0.0001863596,0.0004798001,0.0001987563,0.560878,0.0770603,0.0203364,0.007580295,0.3244571],"study_design_scores_gemma":[0.0001249149,0.0001044789,0.0002024093,0.00001033637,0.00001280652,0.0001050727,0.00001500317,0.9768286,0.0118145,0.008819693,0.001950659,0.00001163932],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1017802,0.0005110973,0.8903445,0.0002216812,0.00004015585,0.0001128891,0.0007169701,0.00421522,0.002057297],"genre_scores_gemma":[0.2885255,0.0001256391,0.7073022,0.0001099174,0.00003020508,0.0002425529,0.001854785,0.0004409825,0.001368249],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002994957,"threshold_uncertainty_score":0.01001912,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03514694564220968,"score_gpt":0.2618498593336783,"score_spread":0.2267029136914686,"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."}}