{"id":"W2144514889","doi":"10.1093/bioinformatics/btl125","title":"Dragon Promoter Mapper (DPM): a Bayesian framework for modelling promoter structures","year":2006,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Genomics and Chromatin Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Promoter; Enhancer; Motif (music); Computational biology; Sequence motif; Gene; Bayesian probability; Computer science; Biology; Genetics; Artificial intelligence; Transcription factor; Gene expression; 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":[0.0001147811,0.0002274601,0.0001694196,0.00004634708,0.0001269866,0.0001023566,0.000224676,0.0003000931,0.00001236598],"category_scores_gemma":[0.00001842259,0.0002008844,0.000136104,0.00005435402,0.00004746019,0.000009084562,0.00008327394,0.00009987958,0.000007914778],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002056584,"about_ca_system_score_gemma":0.00005693878,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008499754,"about_ca_topic_score_gemma":0.000008562669,"domain_scores_codex":[0.9988914,0.000009739014,0.0004132683,0.0001973867,0.0001259177,0.0003623407],"domain_scores_gemma":[0.9992815,0.00001023521,0.0001631138,0.0003985351,0.00008287786,0.00006378887],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001352234,0.00174442,0.005932717,0.007736326,0.001474903,0.00002285749,0.008294619,0.3388706,0.225671,0.2759624,0.05488377,0.0780541],"study_design_scores_gemma":[0.0009368117,0.0004102707,0.0002308388,0.00006525856,0.00005797946,0.00003892095,0.00020752,0.8638594,0.02902368,0.07163943,0.03273133,0.0007985906],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2536875,0.00008478384,0.7445284,0.0001022502,0.0001545845,0.0004957023,0.00007859942,0.00002276659,0.0008454256],"genre_scores_gemma":[0.546045,0.000014823,0.4526012,0.0001931055,0.0003273053,0.0000355904,0.0004105248,0.00003662317,0.0003358383],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5249888,"threshold_uncertainty_score":0.8191829,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007655762186004872,"score_gpt":0.2224709381756465,"score_spread":0.2148151759896417,"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."}}