{"id":"W2140580533","doi":"10.1186/s40168-015-0073-x","title":"BioMiCo: a supervised Bayesian model for inference of microbial community structure","year":2015,"lang":"en","type":"article","venue":"Microbiome","topic":"Gut microbiota and health","field":"Biochemistry, Genetics and Molecular Biology","cited_by":105,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University; Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Universities Space Research Association; Canadian Institutes of Health Research; Tula Foundation","keywords":"Microbiome; Prior probability; Inference; Dirichlet distribution; Bayesian probability; Bayesian inference; Community structure; Biology; Artificial intelligence; Abundance (ecology); Machine learning; Sample (material); Metagenomics; Human microbiome; Relative species abundance; Ecology; Computer science; Mathematics; Bioinformatics","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":[0.005623985,0.001399987,0.001863424,0.001704902,0.000982153,0.001678489,0.004407148,0.002407786,0.003231931],"category_scores_gemma":[0.01694377,0.001499793,0.00221495,0.001241783,0.001701323,0.002020841,0.001865102,0.003889181,0.001247395],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002386435,"about_ca_system_score_gemma":0.002555477,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0287187,"about_ca_topic_score_gemma":0.0341202,"domain_scores_codex":[0.9980976,0.0008342721,0.00009380908,0.000565393,0.0002644439,0.0001444711],"domain_scores_gemma":[0.9905823,0.006843281,0.0008178361,0.0005618965,0.0008993783,0.0002952575],"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":[0.000308567,0.0001391408,0.009255058,0.0001615857,0.0002114501,0.0001121141,0.0002144018,0.9026821,0.00143758,0.01907582,0.004879301,0.06152282],"study_design_scores_gemma":[0.00001330515,0.00001065286,0.0002551087,0.000009827962,0.000006783198,0.00001227737,0.000004079429,0.9906108,0.0001192234,0.008615533,0.0003352675,0.000007029108],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02186461,0.0005080084,0.9730229,0.000576658,0.00006360982,0.0001425441,0.001425964,0.001465491,0.0009302786],"genre_scores_gemma":[0.5091832,0.0007595127,0.4728831,0.001198723,0.0004254203,0.001433877,0.007575708,0.0005461115,0.005994327],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0287187,"threshold_uncertainty_score":0.0571031,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03994741837461974,"score_gpt":0.2998954274602382,"score_spread":0.2599480090856185,"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."}}