{"id":"W3084413028","doi":"10.1371/journal.pcbi.1008108","title":"Generalized estimating equation modeling on correlated microbiome sequencing data with longitudinal measures","year":2020,"lang":"en","type":"article","venue":"PLoS Computational Biology","topic":"Gut microbiota and health","field":"Biochemistry, Genetics and Molecular Biology","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"Public Health Ontario; University of Toronto; Princess Margaret Cancer Centre","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; Princess Margaret Cancer Foundation","keywords":"Generalized estimating equation; Microbiome; Gee; Multivariate statistics; Structural equation modeling; Correlation; Longitudinal study; Operational taxonomic unit; Statistics; Longitudinal data; Biology; Computer science; Mathematics; Data mining; Bioinformatics; Genetics","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":[],"consensus_categories":[],"category_scores_codex":[0.0001551687,0.0001704424,0.0001918853,0.0000463923,0.0001474465,0.00002268126,0.0002536315,0.000130397,0.00001296946],"category_scores_gemma":[0.0001375722,0.0001480866,0.00002818449,0.0001162568,0.0000555803,0.000007553207,0.0001360202,0.0001369145,0.00002773244],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003286678,"about_ca_system_score_gemma":0.0002593935,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002941445,"about_ca_topic_score_gemma":0.000008938297,"domain_scores_codex":[0.9986905,0.00011406,0.0002893127,0.0005774475,0.00009738613,0.0002312783],"domain_scores_gemma":[0.9993516,0.00003596544,0.0001293123,0.0002268832,0.0001658074,0.00009046055],"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":[0.0002241667,0.00003037718,0.001089734,0.0000250002,0.0001053821,0.000002955973,0.00007492599,0.5124584,0.4850996,0.0002813454,0.000278526,0.0003295462],"study_design_scores_gemma":[0.0008283526,0.0004808939,0.0001660397,0.00003607212,0.00003030898,0.00003101084,0.0000221403,0.9948486,0.003022814,0.0001790427,0.0001434695,0.0002112543],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.657612,0.0001106073,0.3409036,0.0009429919,0.00006413975,0.0002041966,0.00009878763,0.0000312787,0.00003243608],"genre_scores_gemma":[0.9122419,0.000006823054,0.07966855,0.001511255,0.0002793746,0.000007904188,0.006257955,0.00002096873,0.000005277017],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4823901,"threshold_uncertainty_score":0.6038795,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1592869646838081,"score_gpt":0.3132232080522324,"score_spread":0.1539362433684243,"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."}}