{"id":"W2130907642","doi":"10.1186/gb-2006-7-7-r63","title":"A strategy for extracting and analyzing large-scale quantitative epistatic interaction data","year":2006,"lang":"en","type":"article","venue":"Genome biology","topic":"Fungal and yeast genetics research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":329,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canada Research Chairs; University of Toronto","funders":"Canadian Institutes of Health Research; Burroughs Wellcome Fund; Howard Hughes Medical Institute; David and Lucile Packard Foundation","keywords":"Epistasis; Computational biology; Identification (biology); Biology; Mutation; Genetics; Gene","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":[0.002596436,0.001846569,0.00102452,0.002550351,0.0007485293,0.001547537,0.001475776,0.0008257004,0.001911807],"category_scores_gemma":[0.01003474,0.0006605904,0.001162587,0.002715718,0.0004859054,0.0009608782,0.001010782,0.00234169,0.001648645],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002827206,"about_ca_system_score_gemma":0.0009859242,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007806601,"about_ca_topic_score_gemma":0.001344339,"domain_scores_codex":[0.9984811,0.0003146761,0.0002024275,0.0003846652,0.0005556167,0.00006148424],"domain_scores_gemma":[0.994231,0.002864864,0.0005180216,0.001462568,0.0007520054,0.0001715804],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004400069,0.0005564044,0.006090167,0.0005620048,0.000427493,0.0004364678,0.0002607772,0.005854683,0.5772781,0.008549098,0.005305855,0.3942389],"study_design_scores_gemma":[0.0002952178,0.0009037341,0.02984717,0.0000959864,0.0005131966,0.003714423,0.0002859824,0.3372147,0.5231019,0.0559579,0.0475383,0.0005315819],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004990404,0.00003161935,0.9902309,0.00005668965,0.00001533564,0.0001142962,0.001155911,0.003242412,0.0001625242],"genre_scores_gemma":[0.02200727,0.0000512925,0.9744121,0.00005986199,0.00001472689,0.0004560501,0.002546953,0.0002368631,0.000214862],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002596436,"threshold_uncertainty_score":0.01373142,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06847567182989736,"score_gpt":0.3804634189240846,"score_spread":0.3119877470941872,"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."}}