{"id":"W2098880867","doi":"10.1534/g3.114.010868","title":"The Insertion Green Monster (iGM) Method for Expression of Multiple Exogenous Genes in Yeast","year":2014,"lang":"en","type":"article","venue":"G3 Genes Genomes Genetics","topic":"Redox biology and oxidative stress","field":"Biochemistry, Genetics and Molecular Biology","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lunenfeld-Tanenbaum Research Institute; University of Toronto","funders":"National Institute of General Medical Sciences; Canada Excellence Research Chairs, Government of Canada; National Human Genome Research Institute; National Institute on Aging; Defense Advanced Research Projects Agency; Advanced Research Projects Agency; National Institutes of Health; Canadian Institute for Advanced Research; Ellison Medical Foundation; Avon Foundation for Women; Krembil Foundation; U.S. Department of Energy","keywords":"Gene; Biology; Yeast; Saccharomyces cerevisiae; Genetics; Selenoprotein; Heterologous expression; Genome; Computational biology; Methionine sulfoxide; Methionine; Recombinant DNA; Biochemistry; Enzyme","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":[0.00044403,0.000208873,0.0002263624,0.00005757068,0.0001609166,0.00001399823,0.0003760339,0.0002469395,0.000003490025],"category_scores_gemma":[0.00002591061,0.0001561953,0.0001284232,0.00008638737,0.0001489734,0.000003262291,0.0001788794,0.0000650656,0.000002793909],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001089055,"about_ca_system_score_gemma":0.00004777824,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002802582,"about_ca_topic_score_gemma":0.0003819086,"domain_scores_codex":[0.9985098,0.0002646423,0.0003822647,0.000398087,0.0001035596,0.000341608],"domain_scores_gemma":[0.9989867,0.00009834411,0.0001851043,0.0005294647,0.0001402894,0.0000601343],"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":[0.0001374512,0.0000275672,0.01876337,0.00002152706,0.00002118964,1.793845e-7,0.00006473192,0.0005529076,0.5905471,0.00001371245,0.00003612313,0.3898141],"study_design_scores_gemma":[0.0006900742,0.000326739,0.006565921,0.00001040329,0.00001756001,0.000004724659,0.0001297186,0.001262662,0.8099843,0.0002283538,0.1805905,0.0001890009],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7656841,0.1751877,0.05778521,0.0001020051,0.0002973399,0.0006622066,0.00008817262,0.00001022719,0.0001829869],"genre_scores_gemma":[0.9471282,0.02926213,0.0222745,0.0001200722,0.0003939508,0.0001370014,0.0001854581,0.00004140754,0.0004573287],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3896251,"threshold_uncertainty_score":0.6369457,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01602476358123056,"score_gpt":0.2764225771229233,"score_spread":0.2603978135416927,"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."}}