{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002115089,0.0001720529,0.0001392796,0.00006519422,0.0001139706,0.0001668341,0.00006699524,0.0001542538,0.00006790906],"category_scores_gemma":[0.0001399386,0.0001561477,0.00004838815,0.00009712922,0.00003673411,0.0000473905,0.00003007042,0.0001373522,0.00000294956],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002632579,"about_ca_system_score_gemma":0.00003199903,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001911912,"about_ca_topic_score_gemma":0.00001134451,"domain_scores_codex":[0.9990742,0.00005413583,0.0003298034,0.0002062446,0.0001387263,0.0001968403],"domain_scores_gemma":[0.9997132,0.00003530483,0.00008735893,0.0001068017,0.00002100707,0.0000362757],"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":[0.00007859245,0.000007157663,0.003317185,0.000246787,0.00001013449,8.453216e-7,0.0007511244,0.00002638552,0.9880308,0.00003837317,0.000002165713,0.007490428],"study_design_scores_gemma":[0.0004578297,0.0003048978,0.003578031,0.0002137066,0.00001437136,0.000009352029,0.00009961512,0.1996098,0.7919898,0.00003966881,0.003398645,0.0002843092],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9909404,0.00006632321,0.008147532,0.0001429874,0.0001925816,0.0002434029,0.0001334256,0.00005058378,0.00008277196],"genre_scores_gemma":[0.9960337,0.00008456458,0.002698846,0.0001453683,0.0001022676,0.0000119489,0.0008795697,0.00002067402,0.00002312038],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1995834,"threshold_uncertainty_score":0.636752,"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."}}