{"id":"W4298125100","doi":"10.1093/bioinformatics/btac655","title":"Integrating phylogenetic and functional data in microbiome studies","year":2022,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Gut microbiota and health","field":"Biochemistry, Genetics and Molecular Biology","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University; McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs; Tel Aviv University; Israel Science Foundation","keywords":"Phylogenetic tree; Microbiome; Computational biology; Metagenomics; Biology; Computer science; Evolutionary biology; Data science; Bioinformatics; Genetics; Gene","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":[0.0002884104,0.00008588694,0.0001002863,0.00006175524,0.0001473057,0.00001331182,0.0001544713,0.00003167189,0.00001698307],"category_scores_gemma":[0.00004183784,0.00008108657,0.00001332305,0.00009447923,0.0000525119,0.000005174858,0.000814958,0.00009637821,0.000003661856],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002410756,"about_ca_system_score_gemma":0.00007951085,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009224532,"about_ca_topic_score_gemma":0.00005470049,"domain_scores_codex":[0.9993744,0.00002845425,0.0002341169,0.0001428578,0.0000635883,0.0001566113],"domain_scores_gemma":[0.9995794,0.00001070733,0.00006805531,0.0002913257,0.0000219783,0.00002851239],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001059179,0.0001510189,0.02199416,0.0004566229,0.0001394193,0.00000513755,0.002542585,0.0001782482,0.9190063,0.0003523171,0.04265486,0.0124134],"study_design_scores_gemma":[0.006382924,0.002128449,0.1148287,0.0001426763,0.0001031083,0.001066233,0.04953326,0.01590501,0.01819885,0.0003917687,0.7894012,0.001917743],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9952407,0.003385206,0.0003311215,0.0002030305,0.0002066263,0.0001680953,0.0001874852,0.000007357252,0.0002703996],"genre_scores_gemma":[0.9871907,0.0005425563,0.009739132,0.001034169,0.00007811759,0.0000178937,0.001131,0.00001171344,0.0002547813],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9008074,"threshold_uncertainty_score":0.3306614,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04460253469201658,"score_gpt":0.2973678979865577,"score_spread":0.2527653632945411,"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."}}