{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006314104,0.00009660691,0.0001325911,0.00008492046,0.00003885643,0.000009184103,0.0001366579,0.0000761631,0.00004232447],"category_scores_gemma":[0.0001280806,0.00008946711,0.00002809076,0.0001329069,0.00006910372,0.000006127817,0.0001264755,0.00005390878,0.000009646975],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000004262203,"about_ca_system_score_gemma":0.00002163228,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00019583,"about_ca_topic_score_gemma":0.00005735838,"domain_scores_codex":[0.999239,0.00006714164,0.0001581922,0.0004056684,0.0000362006,0.00009376793],"domain_scores_gemma":[0.9995543,0.00002338212,0.000070692,0.0002375201,0.00007885655,0.00003528435],"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":[0.0001252329,0.0001253462,0.02359377,0.000009584114,0.001267732,0.000001247563,0.000165159,0.00200663,0.9619737,0.001076885,0.00006229287,0.009592443],"study_design_scores_gemma":[0.001009201,0.001135531,0.5791737,0.0000287136,0.001104469,0.00001935987,0.0001945073,0.09379426,0.30788,0.00683912,0.007776004,0.001045145],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8718215,0.0004210248,0.1272231,0.00002998504,0.0001107545,0.00005310526,0.0002799032,0.00001318206,0.00004741301],"genre_scores_gemma":[0.9420143,0.00009580322,0.05497481,0.00005150278,0.0001037111,0.000004658562,0.002737394,0.000005952828,0.000011798],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6540937,"threshold_uncertainty_score":0.3648363,"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."}}