{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000256158,0.00013906,0.0001117141,0.00003337425,0.0000732869,0.00003586322,0.0004636166,0.00008245303,0.00006643777],"category_scores_gemma":[0.00004711256,0.00008223236,0.00002851846,0.00005409333,0.00004890548,0.00001773738,0.0003314977,0.0000353826,0.00009978993],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001190935,"about_ca_system_score_gemma":0.0001029596,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004163642,"about_ca_topic_score_gemma":0.00002885073,"domain_scores_codex":[0.9990733,0.00002652437,0.0002300775,0.000183461,0.0002098976,0.0002767182],"domain_scores_gemma":[0.9989165,0.00001716363,0.00007615634,0.0008483826,0.00005604806,0.00008575935],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003439028,0.000122594,0.00327816,0.0003948868,0.0002578176,0.00001084716,0.0002958131,0.0007275511,0.747579,0.001038625,0.004373879,0.2415769],"study_design_scores_gemma":[0.001916488,0.0008805239,0.0005043388,0.0002059413,0.00004866447,0.00009194534,0.0005102325,0.02031637,0.8962461,0.00007348804,0.07848937,0.0007165032],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1112504,0.00007551268,0.8851525,0.000158507,0.00008151626,0.0003578574,0.00009627533,0.00003140817,0.002795999],"genre_scores_gemma":[0.4526504,0.0002958616,0.5446411,0.000240918,0.0002603225,0.00003687818,0.0002297989,0.00004889567,0.001595808],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3414,"threshold_uncertainty_score":0.3353339,"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."}}