{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006170208,0.001474849,0.002329795,0.00266602,0.001609775,0.003000362,0.004972495,0.003155075,0.004175771],"category_scores_gemma":[0.02282637,0.001980114,0.003930381,0.002525456,0.002035277,0.003923647,0.002811614,0.00489177,0.001230824],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0022286,"about_ca_system_score_gemma":0.003016455,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0224917,"about_ca_topic_score_gemma":0.02054443,"domain_scores_codex":[0.9971685,0.001283222,0.0001661042,0.0007081389,0.0004808579,0.0001933212],"domain_scores_gemma":[0.9928236,0.005502813,0.0004840133,0.000425307,0.0005105335,0.0002536156],"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":[0.0002997153,0.00009665185,0.008334295,0.0002361044,0.0005160237,0.0002645261,0.0002911847,0.8224153,0.00273589,0.1071366,0.0035876,0.05408616],"study_design_scores_gemma":[0.0000322825,0.0000175212,0.0003460328,0.00002026132,0.00003117849,0.0000545908,0.00001233315,0.9465553,0.0003954516,0.0504419,0.002063682,0.00002934663],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005101441,0.0002790272,0.9925822,0.0001999066,0.00003770961,0.00005462735,0.0006706305,0.0005564982,0.0005179976],"genre_scores_gemma":[0.2279719,0.001306122,0.7570426,0.0007106606,0.0002465303,0.00116285,0.006360502,0.0006816414,0.004517236],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0224917,"threshold_uncertainty_score":0.04472154,"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."}}