{"id":"W2051033712","doi":"10.1371/journal.pone.0050562","title":"Genome Context as a Predictive Tool for Identifying Regulatory Targets of the TetR Family Transcriptional Regulators","year":2012,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Bacterial Genetics and Biotechnology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":75,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"Canadian Institutes of Health Research","keywords":"TetR; Biology; Genetics; Repressor; Gene; Context (archaeology); Protein family; Gene family; Transcriptional regulation; Computational biology; Genome; Gene expression","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003012237,0.0004374447,0.0004052621,0.001392592,0.0002251461,0.0005839875,0.0002034365,0.0002779462,0.00200848],"category_scores_gemma":[0.001623735,0.0001786205,0.0003248599,0.001478284,0.0001361614,0.0003092878,0.0002563271,0.0004843098,0.001110129],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001601141,"about_ca_system_score_gemma":0.0001994237,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001207179,"about_ca_topic_score_gemma":0.001758353,"domain_scores_codex":[0.9998062,0.00004365547,0.00001199956,0.000074156,0.00004805244,0.00001592479],"domain_scores_gemma":[0.9991753,0.0004214573,0.0002002281,0.00005614959,0.00009036475,0.00005644505],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00143966,0.0002061914,0.2693334,0.0005778565,0.0001948938,0.001043379,0.0004098099,0.01318484,0.6162032,0.001267026,0.001494482,0.09464528],"study_design_scores_gemma":[0.00008248199,0.0009431834,0.5749919,0.0001405195,0.0004711235,0.002798866,0.0008099598,0.1608569,0.2315168,0.002322237,0.02495998,0.0001060371],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9104049,0.001648421,0.06541272,0.0001341337,0.00003317195,0.00007737417,0.01343892,0.003651737,0.005198616],"genre_scores_gemma":[0.9218515,0.0003893696,0.06353018,0.00006924789,0.00002208434,0.00005567048,0.01317259,0.0002316725,0.0006777201],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00200848,"threshold_uncertainty_score":0.006718993,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02744244280185239,"score_gpt":0.223150481680491,"score_spread":0.1957080388786386,"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."}}