{"id":"W2019170713","doi":"10.1371/journal.pone.0002907","title":"Genes to Diseases (G2D) Computational Method to Identify Asthma Candidate Genes","year":2008,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Asthma and respiratory diseases","field":"Medicine","cited_by":39,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Chicoutimi; University of Ottawa; McGill University and Génome Québec Innovation Centre; Cégep de Chicoutimi; Ontario Institute for Cancer Research; Université de Montréal; Université Laval","funders":"National Heart, Lung, and Blood Institute; Canadian Institutes of Health Research; National Institutes of Health; Brigham and Women's Hospital; Burroughs Wellcome Fund","keywords":"Candidate gene; Single-nucleotide polymorphism; Genetics; Genetic association; Genome-wide association study; Atopy; Asthma; Genotyping; Gene; Biology; Bioinformatics; Medicine; Genotype; Immunology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.001843012,0.001607494,0.001347191,0.002252021,0.0006604368,0.001230178,0.001720683,0.001187852,0.01254589],"category_scores_gemma":[0.006888222,0.0008016534,0.002851957,0.001952259,0.0004277344,0.0004913892,0.001640261,0.001451861,0.002082803],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004170591,"about_ca_system_score_gemma":0.001994617,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003889241,"about_ca_topic_score_gemma":0.00637331,"domain_scores_codex":[0.9995402,0.0002035976,0.00005227622,0.0001037163,0.00006655337,0.00003354806],"domain_scores_gemma":[0.9972194,0.002350331,0.00008861926,0.0001693892,0.0001030851,0.00006917253],"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":[0.005571941,0.001653003,0.08035289,0.006703706,0.006749986,0.004144445,0.001646934,0.2237921,0.01430408,0.06210145,0.1944392,0.3985402],"study_design_scores_gemma":[0.001715663,0.0002852284,0.008397779,0.0001462687,0.0008466542,0.0008462287,0.0002080754,0.8873085,0.003704916,0.03899832,0.05746484,0.00007743272],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.07971445,0.00129103,0.7955636,0.001865266,0.0005393077,0.001384127,0.05638779,0.05496762,0.008286819],"genre_scores_gemma":[0.1774298,0.0008196512,0.7682353,0.0009431695,0.0001109951,0.004606833,0.04097591,0.003508983,0.003369446],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01254589,"threshold_uncertainty_score":0.04197013,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05164112532281024,"score_gpt":0.3283108057247494,"score_spread":0.2766696804019392,"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."}}