{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008388688,0.00159502,0.002489964,0.002035668,0.001046011,0.002213439,0.004643287,0.00296071,0.005948167],"category_scores_gemma":[0.01814229,0.001088443,0.003521681,0.002970451,0.001729255,0.002042963,0.002324019,0.003784789,0.001879804],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001266893,"about_ca_system_score_gemma":0.002006666,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01819087,"about_ca_topic_score_gemma":0.01223807,"domain_scores_codex":[0.9966814,0.001869519,0.0001547728,0.0007256278,0.0003479514,0.0002206149],"domain_scores_gemma":[0.9900671,0.00831699,0.0004119574,0.0004768815,0.0005586647,0.0001684562],"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":[0.0004124763,0.0001369494,0.006092518,0.0003527867,0.0004167499,0.0004209835,0.0003113222,0.7448717,0.001920827,0.1609936,0.004321196,0.07974885],"study_design_scores_gemma":[0.00002622896,0.00003120608,0.0005610833,0.00003611944,0.00004226707,0.0000623775,0.00001586434,0.963301,0.0002449,0.03373811,0.001907004,0.00003383204],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006600949,0.001011431,0.9904268,0.0004746238,0.0001134613,0.00006617298,0.0003994785,0.0003528339,0.0005543265],"genre_scores_gemma":[0.2832554,0.003583353,0.6905408,0.0008694163,0.0007732133,0.001410253,0.003903155,0.0004194795,0.01524491],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01819087,"threshold_uncertainty_score":0.04436415,"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."}}