{"id":"W2887007046","doi":"10.1101/396085","title":"SNP Variable Selection by Generalized Graph Domination","year":2018,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"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; Mathematics; Feature selection; Overfitting; Interpretability; Combinatorics; Computer science; 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":[0.004316971,0.00112373,0.001707944,0.002037753,0.0008641314,0.000878101,0.001579032,0.0009073777,0.001229594],"category_scores_gemma":[0.00839483,0.00049617,0.001323468,0.001698049,0.001249157,0.0008159054,0.001358259,0.0007273916,0.0001598836],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001575642,"about_ca_system_score_gemma":0.001260324,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005811493,"about_ca_topic_score_gemma":0.004103026,"domain_scores_codex":[0.9975559,0.001690362,0.00004761639,0.0003181262,0.0002409015,0.0001471581],"domain_scores_gemma":[0.9912306,0.00706506,0.0004320199,0.0003874223,0.0006596073,0.0002252982],"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":[0.00008480676,0.0000510883,0.001784435,0.00005351976,0.00008714903,0.0001252612,0.00007647811,0.956179,0.001262264,0.01453959,0.0009886842,0.02476775],"study_design_scores_gemma":[0.000008873757,0.0000164731,0.0001004897,0.000002643812,0.000004963785,0.000009044591,0.000004588938,0.9933926,0.0001301664,0.006201972,0.0001252185,0.000002926017],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1151775,0.0003298212,0.8815156,0.0004310659,0.00004733682,0.000207745,0.0001758976,0.0002594122,0.001855641],"genre_scores_gemma":[0.8433928,0.0002115738,0.15296,0.0002605837,0.00006289574,0.0003691572,0.0004475271,0.00006212954,0.002233248],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005811493,"threshold_uncertainty_score":0.02283061,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01019171667422272,"score_gpt":0.2328965169153496,"score_spread":0.2227048002411269,"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."}}