{"id":"W2979694653","doi":"10.1049/enb.2019.0009","title":"Engineered gene networks enable non‐genetic drug resistance and enhanced cellular robustness","year":2019,"lang":"en","type":"article","venue":"Engineering Biology","topic":"Gene Regulatory Network Analysis","field":"Biochemistry, Genetics and Molecular Biology","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Biology; Gene; Gene regulatory network; Gene expression; Drug resistance; Robustness (evolution); Computational biology; Regulation of gene expression; Biological network; Genetics; Multiple drug resistance; Systems biology","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":[0.0001312033,0.0003427534,0.0001569274,0.0002245589,0.0001183413,0.0004806189,0.0002684383,0.0003390215,0.0005932491],"category_scores_gemma":[0.0004143764,0.0001920198,0.0002021606,0.0001655358,0.0003562959,0.0003813184,0.0002414185,0.0004306962,0.0001352192],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004046841,"about_ca_system_score_gemma":0.000131045,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002336461,"about_ca_topic_score_gemma":0.0003301036,"domain_scores_codex":[0.9999073,0.000015589,0.000005860555,0.000027775,0.00002823807,0.0000153234],"domain_scores_gemma":[0.9998413,0.00005892131,0.000047289,0.0000196494,0.00001708531,0.00001575306],"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":[0.00004486213,0.00003830172,0.0001863942,0.00004394699,0.000009450948,0.00005040345,0.00001575332,0.006984978,0.9808188,0.006306725,0.00005015727,0.005450094],"study_design_scores_gemma":[0.0000212195,0.0001633819,0.0005936742,0.000006828829,0.00001944147,0.00009719733,0.0000133904,0.03945713,0.9524614,0.002826953,0.004327431,0.00001184253],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8217717,0.0005931394,0.1718204,0.0003400924,0.00007328139,0.00007123379,0.0001494818,0.000577941,0.004602638],"genre_scores_gemma":[0.9590216,0.0004760263,0.03844445,0.00005592909,0.000007172932,0.00005966159,0.0001032015,0.00005854067,0.001773446],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0005932491,"threshold_uncertainty_score":0.002936244,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.001798804833766524,"score_gpt":0.1640314562159843,"score_spread":0.1622326513822178,"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."}}