{"id":"W1960602938","doi":"10.1109/cibcb.2015.7300331","title":"Evolutionary computation for disease gene association","year":2015,"lang":"en","type":"article","venue":"","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Brock University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Disease; Gene; Association (psychology); Genetic network; Genetic association; Complex disease; Computational biology; Gene regulatory network; Evolutionary computation; Biology; Genetics; Computer science; Artificial intelligence; Genotype; Single-nucleotide polymorphism; Medicine; Gene expression; Psychology","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":[0.002482004,0.0006415527,0.001339348,0.00165872,0.0009940625,0.001221816,0.001144925,0.001403247,0.002746119],"category_scores_gemma":[0.01467025,0.0004079091,0.0008247957,0.001884595,0.001562606,0.001554844,0.001555027,0.001363697,0.0002679734],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001316431,"about_ca_system_score_gemma":0.001157842,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004671714,"about_ca_topic_score_gemma":0.004390444,"domain_scores_codex":[0.9990078,0.0004864391,0.00005985109,0.0001812922,0.0001860036,0.00007861375],"domain_scores_gemma":[0.9935421,0.005231306,0.0002245387,0.0003456273,0.0004873582,0.0001689865],"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":[0.0001076784,0.00004239425,0.002669112,0.00009483024,0.0001164121,0.0001275641,0.0001208829,0.862887,0.0005310601,0.0748231,0.001171849,0.05730814],"study_design_scores_gemma":[0.00001323146,0.00001046744,0.0003451321,0.000006881838,0.00001083557,0.00002331951,0.00001155223,0.9326233,0.00009981843,0.06636678,0.0004828619,0.000005746324],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06583655,0.00116713,0.9269057,0.00160456,0.0001039799,0.00006337932,0.000198822,0.0003339281,0.003785953],"genre_scores_gemma":[0.6643531,0.0006984553,0.3303163,0.000354033,0.000162592,0.0002782344,0.0005064661,0.0001127568,0.00321799],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004671714,"threshold_uncertainty_score":0.01312619,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01310585119664259,"score_gpt":0.2436517624873376,"score_spread":0.230545911290695,"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."}}