{"id":"W2895977898","doi":"10.1038/s41598-018-33493-w","title":"Discovering Genetic Factors for psoriasis through exhaustively searching for significant second order SNP-SNP interactions","year":2018,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institutes of Health; Memorial University of Newfoundland; Krembil Foundation; University of Toronto; Helene Morgan Babcock and Alfred Babcock Memorial Scholarship Trust; National Psoriasis Foundation; U.S. Department of Veterans Affairs; Foundation for the National Institutes of Health","keywords":"SNP; Computational biology; Psoriasis; Order (exchange); Computer science; Biology; Genetics; Single-nucleotide polymorphism; Gene; Genotype","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009261298,0.0007844544,0.001356968,0.003334551,0.0007811193,0.001095696,0.0006605762,0.0007294456,0.003444997],"category_scores_gemma":[0.00511921,0.0004770414,0.001485857,0.002340622,0.0003267914,0.0007789978,0.0007127812,0.0006388148,0.0006518976],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002816154,"about_ca_system_score_gemma":0.001181461,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00214469,"about_ca_topic_score_gemma":0.005567274,"domain_scores_codex":[0.9992397,0.0001914857,0.00006591305,0.0002354248,0.0001922233,0.00007527788],"domain_scores_gemma":[0.9953039,0.003848507,0.0003662682,0.0002268969,0.0001614696,0.00009294164],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001747291,0.0008253583,0.3052824,0.001722244,0.002274928,0.006979727,0.0005431882,0.206147,0.1031033,0.008373885,0.006863731,0.3561368],"study_design_scores_gemma":[0.000358716,0.0009868154,0.1110048,0.000138507,0.001676985,0.007881761,0.0007036654,0.8005882,0.02357269,0.0432387,0.009668275,0.000180893],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7088723,0.003574664,0.2769273,0.0007391268,0.00005247423,0.0002007292,0.005060586,0.002523406,0.002049425],"genre_scores_gemma":[0.7694939,0.0007334767,0.2183581,0.0002421004,0.00005899884,0.0001352441,0.009985819,0.0001493339,0.0008431235],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003444997,"threshold_uncertainty_score":0.01152468,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03329940974185776,"score_gpt":0.3060789195401956,"score_spread":0.2727795097983379,"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."}}