{"id":"W2999175750","doi":"10.1038/s41596-019-0264-1","title":"Using MicrobiomeAnalyst for comprehensive statistical, functional, and meta-analysis of microbiome data","year":2020,"lang":"en","type":"article","venue":"Nature Protocols","topic":"Gut microbiota and health","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1822,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Profiling (computer programming); Computer science; Microbiome; Protocol (science); Data mining; Data science; Metagenomics; Computational biology; Web application; Human Microbiome Project; Bioinformatics; Human microbiome; World Wide Web; Biology; Medicine","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":[0.01316046,0.004404958,0.003556799,0.005098541,0.002099815,0.004945071,0.003421133,0.00202302,0.02573297],"category_scores_gemma":[0.02404238,0.003447501,0.004780158,0.004618709,0.001555571,0.002652532,0.004298696,0.006284423,0.01273207],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001197204,"about_ca_system_score_gemma":0.006105543,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003688671,"about_ca_topic_score_gemma":0.008630585,"domain_scores_codex":[0.9929471,0.002715482,0.0008462777,0.001728792,0.001293822,0.0004685096],"domain_scores_gemma":[0.9905512,0.00475824,0.0007042494,0.003105561,0.0005785323,0.0003022697],"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":[0.003826502,0.001081809,0.0217547,0.01297332,0.01582663,0.00154598,0.004203276,0.02917289,0.2672799,0.06691163,0.2836245,0.2917989],"study_design_scores_gemma":[0.001363091,0.0006507218,0.01911209,0.0009816778,0.002654031,0.001309126,0.0004034547,0.1447718,0.1839968,0.1214926,0.5219408,0.001323763],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01138098,0.001639947,0.6979366,0.0005144418,0.0008309229,0.001412132,0.08987103,0.1938193,0.002594651],"genre_scores_gemma":[0.02010573,0.0008570097,0.9002602,0.0005207401,0.0001710464,0.009505136,0.03924225,0.02622138,0.00311651],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02573297,"threshold_uncertainty_score":0.08608532,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3441482971478156,"score_gpt":0.4542390359973068,"score_spread":0.1100907388494912,"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."}}