{"id":"W2948018512","doi":"10.1038/s41586-019-1236-x","title":"Longitudinal multi-omics of host–microbe dynamics in prediabetes","year":2019,"lang":"en","type":"article","venue":"Nature","topic":"Gut microbiota and health","field":"Biochemistry, Genetics and Molecular Biology","cited_by":609,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"National Center for Advancing Translational Sciences; National Institute of Environmental Health Sciences; National Human Genome Research Institute; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; National Institute of Diabetes and Digestive and Kidney Diseases; National Heart, Lung, and Blood Institute; National Institutes of Health","keywords":"Prediabetes; Disease; Biology; Transcriptome; Type 2 diabetes; Immune system; Microbiome; Immunology; Omics; Computational biology; Diabetes mellitus; Bioinformatics; Medicine; Genetics; Gene; Internal medicine; Endocrinology; Gene expression","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009375407,0.0001001398,0.0001448261,0.00004561634,0.00001256649,0.000005270788,0.0001513544,0.0009580611,0.00001298812],"category_scores_gemma":[0.00002405282,0.0000958098,0.00005363776,0.00008133312,0.00002840726,0.000002203267,0.00007618321,0.0005823814,0.000009403584],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003613295,"about_ca_system_score_gemma":0.00007188431,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001933686,"about_ca_topic_score_gemma":0.000293211,"domain_scores_codex":[0.9993637,0.00001843763,0.0001473841,0.0002281308,0.00004870756,0.0001936039],"domain_scores_gemma":[0.9996056,0.000006452742,0.00006273125,0.0002353273,0.00005828827,0.00003162291],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00002858134,0.00005667166,0.3291789,0.00006650652,0.00001053111,6.81758e-7,0.00001685571,0.00001017551,0.6699703,0.0001260496,0.0004064286,0.0001282822],"study_design_scores_gemma":[0.001921971,0.0003101485,0.6549771,0.00007001434,0.00001259603,0.00001187202,0.000116771,0.0007202358,0.3324583,0.00002415344,0.009070674,0.0003061978],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9972615,0.001764026,0.00002712816,0.0001393227,0.0002268604,0.0001817422,0.00009629853,0.000004480682,0.0002986611],"genre_scores_gemma":[0.9974356,0.000157878,0.001280544,0.0002815303,0.00004801632,0.00000193344,0.0002283398,0.00001417526,0.0005519267],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3375121,"threshold_uncertainty_score":0.7389445,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004573510618187484,"score_gpt":0.2528593870888109,"score_spread":0.2482858764706235,"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."}}