{"id":"W3086873215","doi":"10.1371/journal.pcbi.1008185","title":"Genetic buffering and potentiation in metabolism","year":2020,"lang":"en","type":"article","venue":"PLoS Computational Biology","topic":"Gene Regulatory Network Analysis","field":"Biochemistry, Genetics and Molecular Biology","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Consejo Superior de Investigaciones Científicas; York University","keywords":"Biology; Pleiotropy; In silico; Reprogramming; Context (archaeology); Saccharomyces cerevisiae; Epistasis; Genetics; Potentiator; Mutation; Mutant; Gene; Mutagenesis; Transcriptome; Computational biology; Phenotype; Cell biology; Gene expression","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":[0.0004862297,0.0004909997,0.0005674714,0.0003268079,0.0002566849,0.0009080998,0.000453504,0.0004929691,0.001705512],"category_scores_gemma":[0.002512589,0.0002666347,0.0007622144,0.0002268222,0.000769558,0.0008726004,0.0009540011,0.0005365154,0.0001237054],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004081028,"about_ca_system_score_gemma":0.0004151339,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001038936,"about_ca_topic_score_gemma":0.0007634258,"domain_scores_codex":[0.9997107,0.00011264,0.00001254575,0.00009403752,0.00003602361,0.00003400306],"domain_scores_gemma":[0.9993488,0.0003671818,0.0001018237,0.0001077574,0.00002162571,0.00005272836],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003503766,0.00006910117,0.008605894,0.0001635264,0.0001422709,0.0003302438,0.00009990906,0.8728814,0.07258329,0.03396182,0.0002162496,0.01059598],"study_design_scores_gemma":[0.00004776303,0.0001763511,0.006846828,0.00001638867,0.00009893344,0.0001654854,0.00003998038,0.9455001,0.01036135,0.03558986,0.001123577,0.00003346567],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8663209,0.0004120875,0.1269752,0.0004152981,0.00004081452,0.00002334917,0.0002284019,0.0003282485,0.005255726],"genre_scores_gemma":[0.9928918,0.0001873823,0.006453061,0.00002841483,0.000007986218,0.00001985427,0.00005460907,0.00002313743,0.0003338757],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001705512,"threshold_uncertainty_score":0.005705476,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01090507023364455,"score_gpt":0.2158714722995162,"score_spread":0.2049664020658716,"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."}}