{"id":"W4200003768","doi":"10.1177/09622802211061634","title":"Functional response regression model on correlated longitudinal microbiome sequencing data","year":2021,"lang":"en","type":"article","venue":"Statistical Methods in Medical Research","topic":"Gut microbiota and health","field":"Biochemistry, Genetics and Molecular Biology","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Public Health Ontario; University of Toronto; Princess Margaret Cancer Centre; University Health Network","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; Princess Margaret Cancer Foundation","keywords":"Functional data analysis; Regression; Regression analysis; Microbiome; Estimator; Computer science; Functional principal component analysis; Functional response; Statistics; Machine learning; Mathematics; Biology; Bioinformatics; Ecology","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":[0.02086842,0.001966739,0.001860604,0.001526155,0.0005829678,0.00116075,0.004151169,0.002445637,0.005871149],"category_scores_gemma":[0.03776815,0.0007382474,0.002468841,0.001912141,0.001988577,0.002364307,0.001937747,0.002811539,0.001957916],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00107332,"about_ca_system_score_gemma":0.001723883,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006320782,"about_ca_topic_score_gemma":0.003448306,"domain_scores_codex":[0.9908996,0.006174704,0.0002482403,0.00160392,0.0006393241,0.0004342266],"domain_scores_gemma":[0.9752706,0.01826941,0.001836805,0.002087035,0.00220126,0.0003348042],"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":[0.0006447468,0.0002742962,0.02435265,0.0006212484,0.0007501849,0.000856406,0.0005877228,0.6968235,0.005719152,0.1434953,0.005355658,0.1205191],"study_design_scores_gemma":[0.00003396636,0.0001207931,0.00164303,0.00003036363,0.00006367717,0.0001010306,0.00005492954,0.964028,0.000760297,0.0312731,0.001857898,0.00003293311],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01394658,0.0001925469,0.9836352,0.00057129,0.00005860711,0.00009869764,0.0004596172,0.0004258096,0.0006116491],"genre_scores_gemma":[0.5971059,0.001084325,0.376193,0.001552246,0.0003139782,0.0022079,0.002994672,0.0006363906,0.01791169],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02086842,"threshold_uncertainty_score":0.1103641,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3527100488831351,"score_gpt":0.5862111740909964,"score_spread":0.2335011252078613,"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."}}