{"id":"W2121806138","doi":"10.1038/msb4100187","title":"Programming gene expression with combinatorial promoters","year":2007,"lang":"en","type":"article","venue":"Molecular Systems Biology","topic":"Gene Regulatory Network Analysis","field":"Biochemistry, Genetics and Molecular Biology","cited_by":381,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Sandia National Laboratories; National Institute of General Medical Sciences; National Institutes of Health; Fondation pour la Recherche Médicale; Alberta Heritage Foundation for Medical Research; National Physical Science Consortium; California Institute of Technology","keywords":"Promoter; Biology; Computational biology; Synthetic biology; Gene; Genetics; Cis-regulatory module; Regulation of gene expression; Transcription factor; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005834578,0.0002650437,0.0002963872,0.00009970136,0.0001029927,0.00002999699,0.0002599908,0.000348481,0.000003047926],"category_scores_gemma":[0.00002516104,0.0002147618,0.0001208788,0.0002082579,0.0001334682,0.000002653106,0.0001161298,0.0001046839,0.000008053385],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003088063,"about_ca_system_score_gemma":0.00005650649,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004955323,"about_ca_topic_score_gemma":0.00001797279,"domain_scores_codex":[0.9981404,0.0001883814,0.0003402063,0.0006172637,0.0001636065,0.0005501322],"domain_scores_gemma":[0.9989023,0.000008223285,0.0001838626,0.0006082735,0.0001361311,0.0001611611],"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":[0.0001361961,0.00005596194,0.01040291,0.00002010416,0.000164878,0.0000504125,0.00001667806,0.0003513829,0.9870397,0.000220179,0.00009447805,0.00144714],"study_design_scores_gemma":[0.001040929,0.0008278324,0.0003193515,0.00003077534,0.00006353407,0.0001461971,0.00007935688,0.00006798645,0.9648498,0.00002598998,0.03212978,0.0004184736],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8183196,0.001655104,0.1787676,0.00001961424,0.0004925129,0.0004271698,0.000003088596,0.00003744536,0.0002779672],"genre_scores_gemma":[0.9965181,0.000009241159,0.002574437,0.00004558807,0.0004378026,0.00005064027,0.0002093189,0.00004782887,0.000107035],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1781986,"threshold_uncertainty_score":0.8757733,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004974958743792781,"score_gpt":0.2257753253879027,"score_spread":0.2208003666441099,"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."}}