{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000391934,0.0004191284,0.0003764396,0.0004733378,0.0002711722,0.0005650847,0.0005936807,0.0002152544,0.0007527525],"category_scores_gemma":[0.001034802,0.0002689776,0.0004852226,0.0005127129,0.0007503894,0.0005639585,0.0004441934,0.0004628354,0.0002296481],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005389528,"about_ca_system_score_gemma":0.0003494296,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002429045,"about_ca_topic_score_gemma":0.0004649054,"domain_scores_codex":[0.9996892,0.000075442,0.00002172655,0.0001020783,0.0000719957,0.00003967041],"domain_scores_gemma":[0.9996083,0.0001990561,0.00006983223,0.0000542976,0.00003835902,0.00003012736],"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":[0.0002559001,0.0001376667,0.002567706,0.0004382047,0.00006316472,0.0002646229,0.0001824797,0.2025438,0.5997125,0.1113197,0.0005216763,0.08199259],"study_design_scores_gemma":[0.0000632661,0.0003246178,0.001011192,0.0000502228,0.00007746929,0.0003356353,0.00006348205,0.4515456,0.4796276,0.05827177,0.008575053,0.00005417039],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2272428,0.0003043731,0.7666289,0.00009397054,0.0000222354,0.0001024609,0.0001452031,0.000970445,0.004489499],"genre_scores_gemma":[0.6954823,0.0004544071,0.3016487,0.00006399619,0.00001174701,0.0003312784,0.0003011484,0.0001773434,0.001528931],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0007527525,"threshold_uncertainty_score":0.003910422,"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."}}