{"id":"W2102427034","doi":"10.1093/nar/gkl381","title":"Computational disease gene identification: a concert of methods prioritizes type 2 diabetes and obesity candidate genes","year":2006,"lang":"en","type":"article","venue":"Nucleic Acids Research","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":144,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of Diabetes and Digestive and Kidney Diseases; Canadian Institutes of Health Research; National Institutes of Health; Ministerio de Economía y Competitividad; University of Oxford; Norwegian Biodiversity Information Centre; Medical Research Council; South African Medical Research Council; Ontario Innovation Trust","keywords":"Candidate gene; Biology; Gene; Genetics; Genome; Computational biology; Identification (biology); Genetic linkage; Type 2 diabetes; Polygene; Quantitative trait locus; Bioinformatics; Diabetes mellitus","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":[0.00417217,0.001396849,0.001451846,0.004478365,0.0006683366,0.002057465,0.001378132,0.0005945941,0.001659045],"category_scores_gemma":[0.009239784,0.0005104357,0.001384696,0.003398259,0.0006133962,0.0006966213,0.001433546,0.001038288,0.000468129],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007701886,"about_ca_system_score_gemma":0.001811145,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00316762,"about_ca_topic_score_gemma":0.005900361,"domain_scores_codex":[0.9986196,0.0005848474,0.0001266088,0.0002559581,0.0003449805,0.00006803293],"domain_scores_gemma":[0.9949127,0.003908226,0.0001999674,0.0003718491,0.0004992916,0.0001078636],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0009341457,0.0005380522,0.02854509,0.001671289,0.001539624,0.0004087672,0.0002852548,0.08139054,0.01548741,0.02767408,0.01456266,0.8269631],"study_design_scores_gemma":[0.0005850864,0.0003248602,0.01372541,0.0001905257,0.0008947212,0.0007604782,0.000214021,0.8657274,0.02096617,0.06883892,0.02761474,0.0001577279],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.06292032,0.004583713,0.9181806,0.002381516,0.000184783,0.0006823851,0.001967041,0.004302395,0.004797211],"genre_scores_gemma":[0.1788977,0.002507748,0.8119397,0.0007015546,0.0001278597,0.0006243648,0.003505769,0.0003574917,0.001337804],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004478365,"threshold_uncertainty_score":0.02206486,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02415193982219282,"score_gpt":0.347416994362287,"score_spread":0.3232650545400942,"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."}}