{"id":"W2552619932","doi":"10.1016/j.cels.2016.10.008","title":"Design and Construction of Generalizable RNA-Protein Hybrid Controllers by Level-Matched Genetic Signal Amplification","year":2016,"lang":"en","type":"article","venue":"Cell Systems","topic":"Gene Regulatory Network Analysis","field":"Biochemistry, Genetics and Molecular Biology","cited_by":22,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"National Center for Complementary and Integrative Health; National Institute of General Medical Sciences; Bill and Melinda Gates Foundation; Natural Sciences and Engineering Research Council of Canada; Defense Advanced Research Projects Agency; National Institutes of Health; National Science Foundation","keywords":"Synthetic biology; Software portability; Computer science; Modularity (biology); Orthogonality; Biology; Computational biology; Genetics; Mathematics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004396042,0.0005514564,0.0003610349,0.000370769,0.0002842335,0.0005063477,0.001075974,0.0003882813,0.00101921],"category_scores_gemma":[0.0003869992,0.0003433254,0.0004139505,0.0002908472,0.0004795698,0.0003868256,0.0004694974,0.0007438922,0.0003324581],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005279351,"about_ca_system_score_gemma":0.0002743851,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003395271,"about_ca_topic_score_gemma":0.0007156572,"domain_scores_codex":[0.9997028,0.00003761006,0.00001895803,0.0000878396,0.0001181317,0.00003477151],"domain_scores_gemma":[0.9998183,0.00006274986,0.0000435009,0.00002199318,0.00002712959,0.00002632774],"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":[0.00005835487,0.00003605178,0.00009141693,0.00004855797,0.00001058469,0.0000339502,0.00002236187,0.002043883,0.9904165,0.001888865,0.00007659039,0.005272864],"study_design_scores_gemma":[0.00003391612,0.0001219885,0.0001631588,0.000002759822,0.00001837447,0.00003894722,0.0000104642,0.01361724,0.9837664,0.0003508292,0.001865124,0.00001084465],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4711454,0.0006573324,0.5204907,0.0001920849,0.000112517,0.0005866537,0.0005036002,0.002349013,0.003962753],"genre_scores_gemma":[0.7814649,0.000412974,0.2134905,0.00009292593,0.00001987365,0.0006036484,0.0006446839,0.0001768443,0.003093703],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001075974,"threshold_uncertainty_score":0.003830433,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0114542805941632,"score_gpt":0.1900069228470999,"score_spread":0.1785526422529367,"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."}}