{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001835554,0.001107326,0.001127425,0.001440498,0.0007556727,0.00157726,0.003286522,0.001949077,0.01380618],"category_scores_gemma":[0.004955953,0.001218213,0.001376338,0.001347568,0.0007253299,0.001347661,0.001035185,0.001947878,0.004656215],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001093907,"about_ca_system_score_gemma":0.001616598,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006267339,"about_ca_topic_score_gemma":0.008926296,"domain_scores_codex":[0.9992034,0.0003009708,0.00004252283,0.0002008587,0.0001986495,0.00005363407],"domain_scores_gemma":[0.9987271,0.0008571625,0.0001066641,0.0001080936,0.000124817,0.00007612274],"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":[0.0003851993,0.00006906835,0.003272057,0.0007840708,0.0001419923,0.0004053283,0.0003046404,0.6481186,0.01001543,0.09623183,0.02630076,0.2139709],"study_design_scores_gemma":[0.00003318454,0.00001968044,0.0002874068,0.00004024664,0.0000234749,0.000113874,0.00001960702,0.923877,0.003248815,0.05245153,0.01984919,0.00003599606],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00187676,0.0002422473,0.9928849,0.0001312667,0.00002653748,0.00003026747,0.001730665,0.00231223,0.0007650178],"genre_scores_gemma":[0.09156537,0.0008502464,0.8933579,0.0001260437,0.00007213908,0.0005086942,0.005987202,0.001442953,0.006089469],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01380618,"threshold_uncertainty_score":0.04618621,"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."}}