{"id":"W2167868583","doi":"10.1016/j.pep.2008.01.008","title":"Codon optimization can improve expression of human genes in Escherichia coli: A multi-gene study","year":2008,"lang":"en","type":"article","venue":"Protein Expression and Purification","topic":"RNA and protein synthesis mechanisms","field":"Biochemistry, Genetics and Molecular Biology","cited_by":336,"is_retracted":false,"has_abstract":false,"ca_institutions":"Structural Genomics Consortium; University of Toronto","funders":"Wellcome Trust","keywords":"Codon usage bias; Escherichia coli; Gene; Biology; Heterologous; lac operon; Start codon; Heterologous expression; Plasmid; Expression vector; Mutagenesis; Translational efficiency; Gene expression; Genetics; Release factor; Transfer RNA; Computational biology; Molecular biology; Translation (biology); Mutation; Messenger RNA; Recombinant DNA; RNA; Genome","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":[0.0002715451,0.0001658513,0.0001852284,0.0000804964,0.0001631673,0.00001293661,0.0001433884,0.0001742654,0.00001681738],"category_scores_gemma":[0.00006170312,0.0001487576,0.00003492078,0.0001015275,0.00005224947,0.00001174116,0.0000718255,0.0000728164,7.03168e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001027944,"about_ca_system_score_gemma":0.00003815517,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008212436,"about_ca_topic_score_gemma":0.00001656037,"domain_scores_codex":[0.9986645,0.000246576,0.0003563277,0.0004149418,0.0001648813,0.0001527503],"domain_scores_gemma":[0.9992368,0.000006592927,0.0002387065,0.0003597157,0.00008921835,0.00006895039],"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":[0.0001263769,0.0004008558,0.00193917,0.00002801648,0.000004673023,0.000001373639,0.0004280972,0.0001763738,0.9958473,0.000008014622,0.000009374508,0.001030404],"study_design_scores_gemma":[0.00106121,0.0003663575,0.002873714,0.00005647108,0.000005404873,0.000001484836,0.0002370195,0.0002777521,0.9948314,0.00001798618,0.0001005213,0.000170661],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9846064,0.0006779903,0.01313099,0.00002711185,0.00003657068,0.001454776,0.0000108532,0.00001455601,0.00004068197],"genre_scores_gemma":[0.9802508,0.0002039369,0.01859578,0.00001439098,0.00004872378,0.0004847831,0.00007380324,0.00002363364,0.0003041928],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005464796,"threshold_uncertainty_score":0.6066161,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02271380499168477,"score_gpt":0.2596540741967334,"score_spread":0.2369402692050486,"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."}}