{"id":"W2952824708","doi":"10.1371/journal.pone.0203242","title":"SNP variable selection by generalized graph domination","year":2019,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Ministry of Forests, Lands and Natural Resource Operations; Genome British Columbia; University of Alberta; Genome Canada; Cornell Lab of Ornithology; Oklahoma State University; Oklahoma Center for the Advancement of Science and Technology; Genome Alberta; U.S. Department of Agriculture; Division of Civil, Mechanical and Manufacturing Innovation; National Science Foundation","keywords":"Pairwise comparison; Overfitting; Feature selection; Mathematics; Interpretability; Computer science; Combinatorics; Artificial intelligence; Statistics","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":[0.00416077,0.001268464,0.001515265,0.002033966,0.000901924,0.0009370436,0.001870295,0.001023718,0.001309345],"category_scores_gemma":[0.009286215,0.0005624474,0.001749874,0.001586222,0.001363749,0.0009598361,0.001405245,0.0008473672,0.0001859179],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001811049,"about_ca_system_score_gemma":0.001308567,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004790274,"about_ca_topic_score_gemma":0.003705149,"domain_scores_codex":[0.997626,0.001577739,0.00005114219,0.0003617576,0.0002443448,0.0001389204],"domain_scores_gemma":[0.9908351,0.007344429,0.0005755711,0.0004041172,0.0006005389,0.0002403289],"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":[0.00006702484,0.00004144343,0.002010773,0.00005748738,0.0000851168,0.0001268848,0.00009279687,0.9640428,0.0009066052,0.01338681,0.0008174233,0.01836475],"study_design_scores_gemma":[0.000008274379,0.000018855,0.0001239799,0.000003196105,0.000006804404,0.00001592285,0.000006214636,0.9912497,0.000123622,0.008294506,0.0001457168,0.000003233632],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1065244,0.0002839705,0.890313,0.0003834104,0.00003498806,0.0002105379,0.0002199445,0.0002317448,0.00179817],"genre_scores_gemma":[0.8134186,0.0002897245,0.1824957,0.000252793,0.00006478721,0.00052125,0.0006616316,0.00007264887,0.002222902],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004790274,"threshold_uncertainty_score":0.02200454,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01572440251090698,"score_gpt":0.2285514446774046,"score_spread":0.2128270421664976,"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."}}