{"id":"W2905818180","doi":"","title":"Higher-order epistatic networks underlie the evolutionary fitness landscape of a xenobiotic- degrading enzyme","year":2019,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Pharmacogenetics and Drug Metabolism","field":"Pharmacology, Toxicology and Pharmaceutics","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Epistasis; Fitness landscape; Biology; In silico; Enzyme; Function (biology); Computational biology; Adaptive evolution; Molecular evolution; Xenobiotic; Evolutionary biology; Genetics; Phylogenetics; Gene; Biochemistry; Population","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0003707043,0.0003141797,0.0003226296,0.0006757926,0.000383039,0.0006765953,0.0002527751,0.0004124203,0.001071418],"category_scores_gemma":[0.001135497,0.0001936074,0.0003582663,0.0004522542,0.0004912529,0.0005020339,0.0004294763,0.0005365298,0.0001094105],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000609951,"about_ca_system_score_gemma":0.0002857034,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001649599,"about_ca_topic_score_gemma":0.002996773,"domain_scores_codex":[0.999756,0.0000746503,0.00001504482,0.00006889916,0.000044453,0.00004091822],"domain_scores_gemma":[0.9996093,0.0001766173,0.0001021323,0.00004391346,0.00003233288,0.00003577562],"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":[0.0003527471,0.0001234575,0.06316936,0.000133708,0.0004043849,0.00086716,0.000299026,0.08519801,0.7974439,0.01551654,0.0005370924,0.03595458],"study_design_scores_gemma":[0.00005578855,0.0003207921,0.2860189,0.00001285714,0.0002571639,0.001510411,0.0004307181,0.6156837,0.05151731,0.03958105,0.00450102,0.0001103044],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9784855,0.00009668725,0.01986403,0.00008725373,0.000002450501,0.00001118173,0.0001271346,0.00007599245,0.001249838],"genre_scores_gemma":[0.9944131,0.00006869211,0.005156182,0.00002530012,0.00000248408,0.00001226428,0.0001104528,0.00001926846,0.0001922797],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001649599,"threshold_uncertainty_score":0.004425526,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05139555612601422,"score_gpt":0.3218341852056817,"score_spread":0.2704386290796675,"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."}}