{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001015037,0.000410133,0.0007194305,0.0008783407,0.0005234617,0.001065627,0.0003535308,0.0005427739,0.001339256],"category_scores_gemma":[0.001321068,0.0002931008,0.0006909085,0.001436369,0.0002238409,0.0005222484,0.001156969,0.00103884,0.0003676114],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002867485,"about_ca_system_score_gemma":0.0003754617,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002943801,"about_ca_topic_score_gemma":0.003925595,"domain_scores_codex":[0.9996064,0.0001049119,0.00002941997,0.0001279811,0.00005137236,0.00007985647],"domain_scores_gemma":[0.9993218,0.0001604863,0.0001486369,0.0001323649,0.0001281358,0.0001085296],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001776672,0.0002770923,0.8957571,0.0004299242,0.001403919,0.0005004596,0.0007159958,0.002755533,0.04920672,0.0009244607,0.005818433,0.04043358],"study_design_scores_gemma":[0.00003085942,0.0002013414,0.9778986,0.00009715754,0.0004041138,0.0004181734,0.0005117701,0.004549819,0.004327634,0.002347016,0.009156693,0.00005675186],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9712638,0.005026826,0.006898916,0.000525426,0.00009540685,0.00002924433,0.01482967,0.0001463643,0.001184448],"genre_scores_gemma":[0.9666424,0.001985531,0.007694245,0.0007160299,0.0001114459,0.000121714,0.02153984,0.00009061905,0.00109816],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002943801,"threshold_uncertainty_score":0.005853355,"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."}}