{"id":"W1994667055","doi":"10.1186/1472-6750-11-105","title":"A bacteria colony-based screen for optimal linker combinations in genetically encoded biosensors","year":2011,"lang":"en","type":"article","venue":"BMC Biotechnology","topic":"Advanced Fluorescence Microscopy Techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"University of Alberta; Cancer Research Institute","keywords":"Biology; Bacteria; Computational biology; Linker; Genetically engineered; Genetics; Genetically modified organism; Biotechnology; Gene; Computer science","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":[0.000952684,0.001299608,0.001060121,0.001334468,0.0002933614,0.0006184746,0.001038833,0.0007903181,0.0013079],"category_scores_gemma":[0.001231742,0.0004807418,0.0007677118,0.001393044,0.0002403867,0.0003522685,0.0009108259,0.0007328361,0.001225459],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004737886,"about_ca_system_score_gemma":0.0005337803,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006384672,"about_ca_topic_score_gemma":0.001387809,"domain_scores_codex":[0.9982817,0.0003360267,0.0002216365,0.0003054954,0.0007260416,0.000129117],"domain_scores_gemma":[0.9990877,0.0003153914,0.0001795408,0.0001248221,0.0002066717,0.00008594785],"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":[0.00005037503,0.0001425132,0.0004979987,0.0000577903,0.00001991191,0.00007489012,0.00002129024,0.0001878168,0.9955755,0.00004788754,0.00006038089,0.00326365],"study_design_scores_gemma":[0.00001513491,0.0005627197,0.002617437,0.000008416845,0.00006502207,0.0005022157,0.00003311212,0.001447453,0.9934134,0.00002784843,0.001294798,0.00001256261],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8942496,0.002003706,0.09296584,0.0004107307,0.00006039374,0.00167423,0.003748193,0.001440738,0.003446434],"genre_scores_gemma":[0.7578951,0.001600409,0.2284756,0.0002397489,0.00001785557,0.001076698,0.006273234,0.0004523451,0.003968968],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001334468,"threshold_uncertainty_score":0.005038381,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02587347413524234,"score_gpt":0.2803946348294825,"score_spread":0.2545211606942402,"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."}}