{"id":"W2094683075","doi":"10.1371/journal.pcbi.1002048","title":"Quantitative Epistasis Analysis and Pathway Inference from Genetic Interaction Data","year":2011,"lang":"en","type":"article","venue":"PLoS Computational Biology","topic":"Microbial Metabolic Engineering and Bioproduction","field":"Biochemistry, Genetics and Molecular Biology","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ottawa Hospital; University of Waterloo; University of Ottawa","funders":"Canadian Institutes of Health Research; Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Epistasis; Computational biology; Biology; Gene regulatory network; Inference; Gene; False positive paradox; Genetic architecture; Saccharomyces cerevisiae; Quantitative trait locus; Systems biology; Genetics; Gene expression; Computer science; Machine learning; Artificial intelligence","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":[0.004221697,0.001290024,0.001135455,0.003074291,0.0004772987,0.001232479,0.001528701,0.0008401986,0.001042731],"category_scores_gemma":[0.01528256,0.0007841013,0.001505413,0.001615085,0.0009765201,0.001623142,0.001179102,0.001519293,0.0002595806],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009517397,"about_ca_system_score_gemma":0.001300855,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003174735,"about_ca_topic_score_gemma":0.003515422,"domain_scores_codex":[0.996865,0.001877577,0.0001543485,0.0005412182,0.0004752616,0.0000866672],"domain_scores_gemma":[0.9905803,0.007495258,0.0007731,0.0007395293,0.0002574353,0.0001543654],"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":[0.0003291504,0.0001791885,0.008923264,0.0003898257,0.0005868198,0.0002162927,0.0001221024,0.8335344,0.0245771,0.04017871,0.0008864159,0.09007667],"study_design_scores_gemma":[0.00001611269,0.00002037375,0.0005907796,0.000004335903,0.00001780681,0.00004346119,0.000007135536,0.9678559,0.00163613,0.02951411,0.0002789077,0.00001500657],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01762463,0.00007306094,0.9810371,0.00007299506,0.000004116811,0.00002091974,0.0003823559,0.0006637224,0.0001212071],"genre_scores_gemma":[0.3964633,0.0001876864,0.6014153,0.00008882494,0.00002163243,0.0001950015,0.001238757,0.0001604724,0.0002289768],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004221697,"threshold_uncertainty_score":0.02232671,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06790970625536714,"score_gpt":0.2943709936295995,"score_spread":0.2264612873742323,"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."}}