{"id":"W2222833576","doi":"10.1186/s13040-015-0062-4","title":"Functional dyadicity and heterophilicity of gene-gene interactions in statistical epistasis networks","year":2015,"lang":"en","type":"article","venue":"BioData Mining","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"National Institute of Allergy and Infectious Diseases; U.S. National Library of Medicine; National Institute of General Medical Sciences; National Cancer Institute; National Institutes of Health","keywords":"Epistasis; Context (archaeology); Computational biology; Biology; Genetic association; Gene; Gene interaction; Gene ontology; Genetics; Computer science; Genotype; Single-nucleotide polymorphism; Gene expression","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":[0.001397959,0.0003070596,0.0004356131,0.002772582,0.0004942809,0.0008968103,0.0004760945,0.0004212188,0.0009503202],"category_scores_gemma":[0.007205646,0.000185863,0.0006888086,0.001871794,0.001060138,0.001011963,0.0007782954,0.0004955595,0.0001052873],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007713767,"about_ca_system_score_gemma":0.0004042275,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002648305,"about_ca_topic_score_gemma":0.003106476,"domain_scores_codex":[0.9990045,0.0004362652,0.00005698505,0.000309423,0.0001274074,0.000065376],"domain_scores_gemma":[0.9929141,0.004725415,0.001449243,0.0004045986,0.0002842898,0.0002223314],"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":[0.000555155,0.0002654022,0.3569558,0.0007399381,0.001108794,0.001028739,0.001620265,0.3640315,0.04044947,0.1387342,0.002473111,0.09203753],"study_design_scores_gemma":[0.00002323705,0.00009496093,0.1014714,0.00003212899,0.0001935744,0.0005746216,0.000357671,0.7661652,0.002989843,0.1263435,0.001716557,0.00003734574],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7110706,0.0006661539,0.285111,0.0003381478,0.00001038472,0.00005233418,0.0007608152,0.0002140273,0.001776515],"genre_scores_gemma":[0.9880144,0.0001272519,0.01130672,0.00002490232,0.00001172207,0.00004078366,0.0003003288,0.0000119055,0.0001619099],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002772582,"threshold_uncertainty_score":0.007393181,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04534004385984268,"score_gpt":0.2740099323237019,"score_spread":0.2286698884638592,"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."}}