{"id":"W4404374604","doi":"10.1093/bib/bbae581","title":"Predicting bacterial transcription factor binding sites through machine learning and structural characterization based on DNA duplex stability","year":2024,"lang":"en","type":"article","venue":"Briefings in Bioinformatics","topic":"RNA and protein synthesis mechanisms","field":"Biochemistry, Genetics and Molecular Biology","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"Dirección General de Asuntos del Personal Académico, Universidad Nacional Autónoma de México; Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior","keywords":"DNA binding site; Computational biology; DNA; Inverted repeat; Base pair; Bacterial transcription; Binding site; Genetics; Biology; Transcription (linguistics); Duplex (building); Transcription factor; Gene; Promoter; Genome; Gene expression","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":[0.0005477397,0.0004450845,0.0004581692,0.001252876,0.0001991115,0.0003232201,0.0002116651,0.000408421,0.0004663079],"category_scores_gemma":[0.001494393,0.0001397865,0.0004520646,0.0005295542,0.0001504512,0.0004185895,0.0001352868,0.0002996329,0.0003136069],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002603862,"about_ca_system_score_gemma":0.0002805459,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001844249,"about_ca_topic_score_gemma":0.001679943,"domain_scores_codex":[0.9997745,0.00006458242,0.00002123468,0.00006758453,0.00004760466,0.00002450795],"domain_scores_gemma":[0.9992156,0.0004576598,0.0001277799,0.00003883488,0.0001211287,0.00003891841],"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":[0.0006955465,0.0003092126,0.07318668,0.0002007477,0.0001336374,0.0001631663,0.00006376397,0.4478704,0.2141742,0.0009013191,0.00110689,0.2611944],"study_design_scores_gemma":[0.000005087175,0.00005766364,0.00599924,0.000003476332,0.00001273745,0.00003844686,0.000009679243,0.9792506,0.01401976,0.0004234896,0.0001710772,0.000008733516],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7831209,0.0008932673,0.2135953,0.0000738954,0.00002175204,0.0000319065,0.0005096279,0.001109529,0.0006439788],"genre_scores_gemma":[0.9369218,0.0001869421,0.06156736,0.00001827185,0.00001392379,0.00002697079,0.0009614236,0.00002524337,0.000278101],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001844249,"threshold_uncertainty_score":0.003666997,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01584679374215488,"score_gpt":0.237394777809497,"score_spread":0.2215479840673421,"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."}}