{"id":"W2155484811","doi":"10.1093/nar/gkl879","title":"PReMod: a database of genome-wide mammalian cis-regulatory module predictions","year":2006,"lang":"en","type":"article","venue":"Nucleic Acids Research","topic":"Genomics and Chromatin Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":89,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Montreal Clinical Research Institute; McGill University and Génome Québec Innovation Centre","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Physicians Committee for Responsible Medicine; Génome Québec; Genome Canada","keywords":"Biology; Genome; Genome browser; Context (archaeology); Gene; Genetics; Computational biology; DNA binding site; Cis-regulatory module; Genomics; Transcription factor; Database; Promoter; Computer science; Enhancer; 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":[0.0005745773,0.0001347417,0.0001461364,0.0001356285,0.0001782358,0.00003046515,0.0004466415,0.0001682997,0.0001049406],"category_scores_gemma":[0.00007348757,0.0001394394,0.00009056328,0.0002140412,0.0002868052,0.00000644811,0.0003886373,0.0002054864,0.00003115585],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005151016,"about_ca_system_score_gemma":0.0001625392,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001450097,"about_ca_topic_score_gemma":0.000108775,"domain_scores_codex":[0.998345,0.000106629,0.0002905305,0.0003997218,0.0004159176,0.0004421591],"domain_scores_gemma":[0.9986809,0.00002114389,0.00006332963,0.0008620899,0.0002612112,0.0001113553],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00008365398,0.0002525114,0.01479654,0.00009541824,0.00004818881,0.000006204504,0.00004194925,0.001651548,0.9724287,0.0008965267,0.009292915,0.00040586],"study_design_scores_gemma":[0.003102773,0.001775324,0.5587901,0.0001002325,0.00006555746,0.00006492412,0.0006338098,0.02572945,0.2807744,0.003860157,0.1240459,0.001057407],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.989172,0.0004011519,0.002379209,0.0001152253,0.00005893963,0.0002828569,0.0002554142,0.00001475661,0.007320384],"genre_scores_gemma":[0.9932618,0.00009191396,0.002384502,0.0000230477,0.0002387162,0.0000301667,0.0005586889,0.00004209362,0.003369066],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6916543,"threshold_uncertainty_score":0.5686174,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01668678522604314,"score_gpt":0.2759709852762874,"score_spread":0.2592842000502443,"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."}}