{"id":"W2909006258","doi":"10.1002/gepi.22190","title":"A finite mixture model for X‐chromosome association with an emphasis on microbiome data analysis","year":2019,"lang":"en","type":"article","venue":"Genetic Epidemiology","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre; Lunenfeld-Tanenbaum Research Institute; Mount Sinai Hospital; Sinai Health System; Public Health Ontario; University Health Network; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Microbiome; Inference; Mechanism (biology); Biology; Genetics; Computational biology; Chromosome; X chromosome; Computer science; Artificial intelligence; Gene; Physics","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.002265973,0.0003409605,0.0008813688,0.000189873,0.0001297783,0.0000126815,0.0007260458,0.0007186201,0.00005557713],"category_scores_gemma":[0.001783358,0.0002916601,0.0002306316,0.0003235082,0.00006626066,0.000007820709,0.0001891243,0.0001696882,0.00005123984],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000078631,"about_ca_system_score_gemma":0.0001528287,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004734402,"about_ca_topic_score_gemma":0.0003320019,"domain_scores_codex":[0.9963207,0.0007026531,0.0006879989,0.001388203,0.0001123948,0.0007881093],"domain_scores_gemma":[0.9962654,0.0008464716,0.0006069794,0.001907065,0.0002060032,0.0001680875],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003700095,0.0002025556,0.5904103,0.00002755924,0.002318312,9.567817e-7,0.00007247554,0.3777816,0.01543934,0.0001601241,0.01066303,0.002553744],"study_design_scores_gemma":[0.002099269,0.002815487,0.1340254,0.00001245965,0.001256603,0.00001158772,0.00005804753,0.8358557,0.0005295593,0.001889262,0.02061173,0.0008348846],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7083457,0.0004310348,0.2880006,0.001384286,0.0001692499,0.0005973782,0.000893209,0.00002547331,0.0001529936],"genre_scores_gemma":[0.8230054,0.0002626719,0.161138,0.004522903,0.0003245241,0.0001262617,0.007479403,0.00005658444,0.003084236],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4580741,"threshold_uncertainty_score":0.9999536,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.039696150132915,"score_gpt":0.3168662238064153,"score_spread":0.2771700736735003,"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."}}