{"id":"W2156733952","doi":"10.1093/bioinformatics/btn444","title":"Seeder: discriminative seeding DNA motif discovery","year":2008,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Genomics and Chromatin Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":48,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; Centre Sève; Fonds Québécois de la Recherche sur la Nature et les Technologies; Norges Teknisk-Naturvitenskapelige Universitet","keywords":"Discriminative model; Computational biology; Computer science; DNA binding site; Promoter; Benchmark (surveying); Transcription factor; Biology; Data mining; Artificial intelligence; Genetics; Gene; Gene expression","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008361337,0.000175257,0.0001422876,0.00004270147,0.000188478,0.00004929094,0.0002130149,0.0001144563,0.000007130156],"category_scores_gemma":[0.00003726262,0.0001579107,0.00009966541,0.00007142079,0.0001111815,0.00002130827,0.0001792844,0.000075863,0.00002664292],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002592574,"about_ca_system_score_gemma":0.00007527308,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005488548,"about_ca_topic_score_gemma":0.000006385831,"domain_scores_codex":[0.9991431,0.00001060583,0.0002965227,0.0001424174,0.0001456382,0.0002617599],"domain_scores_gemma":[0.9994291,0.0000088038,0.0001302952,0.0002952583,0.00005839521,0.00007813892],"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":[0.0002933031,0.0008527333,0.06120028,0.00111724,0.001051806,0.00008439025,0.02061909,0.003628888,0.8125288,0.01004178,0.06821712,0.02036461],"study_design_scores_gemma":[0.007770184,0.003015433,0.1604441,0.0002966267,0.0003264665,0.002090815,0.01928551,0.2478253,0.3986705,0.001820651,0.1524488,0.006005629],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9743739,0.00008331547,0.01472874,0.00006478019,0.0002063639,0.0001416811,0.00005615839,0.00002081761,0.01032418],"genre_scores_gemma":[0.9883147,0.0002867378,0.009056673,0.0002212124,0.000142348,0.000008048788,0.0002052729,0.00002218998,0.001742814],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4138583,"threshold_uncertainty_score":0.6439412,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01210120642419581,"score_gpt":0.2174705794309284,"score_spread":0.2053693730067326,"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."}}