{"id":"W2125810118","doi":"10.1093/bioinformatics/btm224","title":"RankMotif++: a motif-search algorithm that accounts for relative ranks of K-mers in binding transcription factors","year":2007,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Genomics and Chromatin Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":67,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Ontario Genomics; Ontario Genomics Institute; Genome Canada","keywords":"Motif (music); DNA binding site; Computational biology; Computer science; Algorithm; Data mining; Biology; Genetics; Gene","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":[0.001186387,0.001709107,0.001269658,0.001088864,0.0008046684,0.0008533994,0.003275996,0.001498875,0.00435398],"category_scores_gemma":[0.003601667,0.0006847193,0.001025885,0.001027221,0.0006130583,0.001715643,0.00119446,0.001539992,0.002220305],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007559565,"about_ca_system_score_gemma":0.002050289,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004608281,"about_ca_topic_score_gemma":0.009007965,"domain_scores_codex":[0.9992968,0.0001586163,0.00003093309,0.000249194,0.0002017915,0.00006257972],"domain_scores_gemma":[0.9991442,0.000382639,0.00009478046,0.000141503,0.0001687008,0.0000682184],"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":[0.001027398,0.0004831208,0.008595895,0.0007332354,0.0002754058,0.0001869061,0.0001953281,0.3586853,0.03029367,0.01725413,0.04927297,0.5329965],"study_design_scores_gemma":[0.00008651289,0.0001047628,0.0003189485,0.00001524362,0.00002225773,0.0001105565,0.00001948688,0.9754283,0.008522511,0.00940007,0.005942732,0.00002860834],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04032476,0.00085617,0.9202634,0.0003643372,0.0001124631,0.000211723,0.001628973,0.03409479,0.002143318],"genre_scores_gemma":[0.1447848,0.000276981,0.8458118,0.0002944844,0.00006883289,0.0003864708,0.003609153,0.001698376,0.003069011],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004608281,"threshold_uncertainty_score":0.01456547,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02064250354701683,"score_gpt":0.2618901333386715,"score_spread":0.2412476297916547,"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."}}