{"id":"W2005546833","doi":"10.1371/journal.pcbi.0040005","title":"In Silico Detection of Sequence Variations Modifying Transcriptional Regulation","year":2008,"lang":"en","type":"article","venue":"PLoS Computational Biology","topic":"RNA and protein synthesis mechanisms","field":"Biochemistry, Genetics and Molecular Biology","cited_by":100,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Ontario Institute for Cancer Research; Child and Family Research Institute; Princess Margaret Cancer Centre; University of British Columbia","funders":"Canadian Institutes of Health Research; Vetenskapsrådet; Stockholms Läns Landsting; University of Toronto; Michael Smith Health Research BC","keywords":"In silico; Biology; Genetics; Computational biology; DNA binding site; Gene; Enhancer; Genetic variation; Regulatory sequence; Transcription factor; Single-nucleotide polymorphism; Bioinformatics; Promoter; Gene expression; Genotype","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000984678,0.00007686055,0.0001042669,0.00008857901,0.00006663505,0.000002048965,0.00007340859,0.0001218643,0.000032781],"category_scores_gemma":[0.00006406861,0.0000813692,0.00004528838,0.0001039158,0.00007134179,0.000007630251,0.00001418925,0.00004308918,0.000006016971],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001599959,"about_ca_system_score_gemma":0.00006541059,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000203795,"about_ca_topic_score_gemma":0.000009498049,"domain_scores_codex":[0.9992804,0.0001038191,0.0002286463,0.0002017159,0.00008320321,0.0001022341],"domain_scores_gemma":[0.9996691,0.0000431291,0.00008263576,0.00008389055,0.00009890932,0.00002229616],"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":[0.00004877814,0.00005811729,0.001028691,0.000006736745,0.00001813181,5.659654e-7,0.00005366093,0.00904595,0.9830559,0.006085201,0.000004408415,0.0005938094],"study_design_scores_gemma":[0.0005965464,0.0002583516,0.03198699,0.00001279022,0.000009075907,0.00005192676,0.00001062749,0.02002979,0.9113389,0.03511298,0.000414746,0.0001772705],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8069944,0.0001083439,0.1924269,0.0001245997,0.00007138905,0.0001265006,0.0000238436,0.00000724204,0.0001167518],"genre_scores_gemma":[0.9920214,0.00002420574,0.007523301,0.00008833074,0.00007840367,0.00003251149,0.0001860805,0.000007002913,0.00003877294],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.185027,"threshold_uncertainty_score":0.331814,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04158741154063465,"score_gpt":0.2615315130370233,"score_spread":0.2199441014963886,"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."}}