{"id":"W2018446288","doi":"10.1371/journal.pcbi.1003918","title":"BiomeNet: A Bayesian Model for Inference of Metabolic Divergence among Microbial Communities","year":2014,"lang":"en","type":"article","venue":"PLoS Computational Biology","topic":"Gut microbiota and health","field":"Biochemistry, Genetics and Molecular Biology","cited_by":39,"is_retracted":false,"has_abstract":true,"ca_institutions":"Acadia University; Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Tula Foundation","keywords":"Metagenomics; Inference; Gibbs sampling; Bayesian inference; Computational biology; Microbiome; Bayesian probability; Human Microbiome Project; Taxonomic rank; Biology; Computer science; Community structure; Metabolic pathway; Artificial intelligence; Bioinformatics; Human microbiome; Ecology; Taxon; Gene; Genetics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001252126,0.0001166277,0.0002022868,0.00006685661,0.00009936716,0.000005921283,0.0002129811,0.0001253702,0.00001395467],"category_scores_gemma":[0.00003964925,0.000112678,0.00006883733,0.00004626554,0.000228627,0.000003075114,0.0001037047,0.00005197339,0.000003241728],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000004150763,"about_ca_system_score_gemma":0.00009962258,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006021582,"about_ca_topic_score_gemma":0.0001111547,"domain_scores_codex":[0.9992584,0.00009014017,0.0002408653,0.0001786955,0.0000380326,0.0001938796],"domain_scores_gemma":[0.9994218,0.00008190254,0.0001251542,0.0001588307,0.0001649385,0.00004739291],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00009516073,0.0001573969,0.04647861,0.00008198291,0.0001200618,3.35858e-8,0.0003438516,0.0231023,0.9102051,0.01835283,0.0003288113,0.0007339197],"study_design_scores_gemma":[0.00195917,0.0009455961,0.05666653,0.00004295921,0.00007843626,0.000005388694,0.00009927133,0.8497757,0.06564362,0.0202329,0.003955972,0.0005944357],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.695653,0.0001223127,0.3037058,0.00006976664,0.00005026977,0.000149515,0.000191828,0.000006454729,0.00005106302],"genre_scores_gemma":[0.9817799,0.00004417346,0.01664812,0.0003202251,0.0000711877,0.00002524281,0.001057922,0.00001016573,0.00004302636],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8445614,"threshold_uncertainty_score":0.4594876,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02528527794603092,"score_gpt":0.284290984786656,"score_spread":0.2590057068406251,"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."}}