{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00003750309,0.00008292119,0.000121016,0.00004021879,0.00002735749,0.000007785477,0.00006592511,0.00007453683,0.00001170691],"category_scores_gemma":[0.00003291525,0.0000858074,0.00003225251,0.00009920388,0.0000388691,0.000001826035,0.00007362559,0.00004145476,0.000006408301],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000004974135,"about_ca_system_score_gemma":0.00002223309,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004687806,"about_ca_topic_score_gemma":0.000006514374,"domain_scores_codex":[0.9993495,0.00006786485,0.0001531911,0.0002656416,0.00004768399,0.0001160889],"domain_scores_gemma":[0.9997907,0.00001247491,0.00004394686,0.00005935396,0.00003789796,0.0000555979],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00007816227,0.00006179131,0.1933324,0.00002827389,0.0002576942,0.00000504873,0.0001406438,0.1938372,0.6039278,0.001073988,0.0002103716,0.007046584],"study_design_scores_gemma":[0.001097669,0.0001736736,0.7730222,0.000005902206,0.00006993819,0.00001445757,0.00003112369,0.2089391,0.009825105,0.003391861,0.003080298,0.0003486557],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9850279,0.001432406,0.012713,0.00068028,0.00002332314,0.00006880949,0.000004952532,0.00000853941,0.00004075334],"genre_scores_gemma":[0.9943024,0.0000588365,0.004692235,0.0006209829,0.000174879,0.000007622612,0.000128617,0.000008604023,0.000005852018],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5941027,"threshold_uncertainty_score":0.3499124,"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."}}