{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002514328,0.00009828845,0.0001341709,0.00004088408,0.00003350321,0.00001721909,0.00008495055,0.00007370537,0.00001048877],"category_scores_gemma":[0.00006658953,0.00009698726,0.00002214693,0.00006264519,0.00009915008,0.000008205081,0.000128967,0.00008352226,0.000001699419],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001557984,"about_ca_system_score_gemma":0.00004469246,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007665505,"about_ca_topic_score_gemma":0.0001520249,"domain_scores_codex":[0.9992716,0.00003397475,0.0002709529,0.0001876317,0.00007451219,0.0001612947],"domain_scores_gemma":[0.999541,0.00002520308,0.00008391117,0.0002011472,0.00004470428,0.0001039648],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.003301064,0.001084896,0.3741522,0.0002466036,0.0009038185,0.00006484165,0.002095484,0.02020334,0.2597431,0.002675282,0.1261795,0.2093499],"study_design_scores_gemma":[0.01306244,0.002928176,0.4550058,0.0003084221,0.0003289448,0.0008790327,0.003799555,0.3356251,0.1014146,0.001661732,0.08171199,0.003274205],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8859716,0.0005469391,0.1125762,0.00006200327,0.0002218276,0.00008350639,0.0002483056,0.000004547419,0.0002851194],"genre_scores_gemma":[0.9878322,0.0000578871,0.01098473,0.0001186561,0.0001598375,0.000005541767,0.0007998287,0.00000767144,0.00003367863],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3154218,"threshold_uncertainty_score":0.3955026,"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."}}