{"id":"W2115490334","doi":"10.3389/fgene.2014.00011","title":"Exploring the potential benefits of stratified false discovery rates for region-based testing of association with rare genetic variation","year":2014,"lang":"en","type":"article","venue":"Frontiers in Genetics","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Jewish General Hospital","funders":"National Institute for Health and Care Research","keywords":"Linkage disequilibrium; Metric (unit); Exome; Computer science; False discovery rate; Genetic association; Genome-wide association study; Annotation; Statistical power; Exome sequencing; Data mining; Computational biology; Biology; Genetics; Statistics; Mutation; Artificial intelligence; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.3030883,0.002459806,0.002536512,0.002494672,0.001867249,0.004166444,0.005172043,0.004300125,0.002932568],"category_scores_gemma":[0.5825609,0.001489938,0.005615836,0.002775621,0.005904245,0.007355464,0.003879437,0.007638139,0.0006067132],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003048777,"about_ca_system_score_gemma":0.003600324,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005321677,"about_ca_topic_score_gemma":0.00468894,"domain_scores_codex":[0.7858549,0.1861644,0.006238758,0.01128865,0.008429468,0.002023803],"domain_scores_gemma":[0.2573117,0.6994351,0.01134968,0.02434711,0.006693446,0.0008629545],"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":[0.006655658,0.0006116481,0.162231,0.002853263,0.01267593,0.003021192,0.006458989,0.2607423,0.01667021,0.2069689,0.006011165,0.3150997],"study_design_scores_gemma":[0.001087195,0.002964111,0.04104725,0.001058881,0.003563929,0.001940504,0.0007078124,0.5235226,0.01923372,0.393857,0.01039469,0.0006222717],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.06214858,0.002762021,0.9254431,0.003880121,0.0005602987,0.0006253066,0.0004673805,0.001004608,0.003108542],"genre_scores_gemma":[0.6194373,0.0007187176,0.3736633,0.002640953,0.0002841108,0.001371161,0.0003952598,0.0004393454,0.001049812],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3030883,"threshold_uncertainty_score":0.8594162,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0281695373448758,"score_gpt":0.2357368606618049,"score_spread":0.2075673233169291,"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."}}