{"id":"W2993476941","doi":"10.1186/s40168-019-0767-6","title":"Advancing functional and translational microbiome research using meta-omics approaches","year":2019,"lang":"en","type":"review","venue":"Microbiome","topic":"Gut microbiota and health","field":"Biochemistry, Genetics and Molecular Biology","cited_by":338,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada; Ministero dello Sviluppo Economico; Ontario Ministry of Economic Development and Innovation; Government of Canada; Canadian Institutes of Health Research; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; Genome Canada; Ontario Genomics; Ontario Genomics Institute; University of Ottawa","keywords":"Microbiome; Biology; Omics; Microbial ecology; Computational biology; Metagenomics; Medical microbiology; Translational research; Posttranslational modification; Bioinformatics; Proteomics; Data science; Biotechnology; Genetics; Microbiology; Bacteria; Computer science; Biochemistry","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":[0.01560036,0.002160398,0.003112114,0.006376037,0.0008748478,0.006070944,0.00224093,0.002373841,0.002641017],"category_scores_gemma":[0.009887232,0.00093386,0.003971304,0.004844152,0.001576517,0.005399848,0.005357642,0.005078803,0.001135141],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002072592,"about_ca_system_score_gemma":0.004258086,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001989859,"about_ca_topic_score_gemma":0.003029527,"domain_scores_codex":[0.9961469,0.00197596,0.0002751404,0.0005907935,0.0007966602,0.0002144581],"domain_scores_gemma":[0.9920488,0.004254698,0.0007947663,0.001129098,0.001275051,0.0004975945],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0005899663,0.0003218849,0.0258813,0.02123269,0.004576105,0.0008043257,0.001684869,0.0322836,0.152575,0.1051122,0.02083317,0.6341048],"study_design_scores_gemma":[0.0001675248,0.0008988398,0.02992696,0.006636832,0.002952979,0.00131173,0.002260167,0.1018153,0.06282195,0.3035712,0.487042,0.0005946634],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"review","genre_scores_codex":[0.02226374,0.2388671,0.7005858,0.0158583,0.00217042,0.000612033,0.00804363,0.003014018,0.008584938],"genre_scores_gemma":[0.08793023,0.1786979,0.717919,0.00446084,0.001428111,0.00079887,0.007154249,0.0005018152,0.001108981],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.01560036,"threshold_uncertainty_score":0.08250362,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.435717386677676,"score_gpt":0.4214016577859939,"score_spread":0.01431572889168203,"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."}}