{"id":"W4408962005","doi":"10.1186/s40168-025-02080-3","title":"Modeling microbiome-trait associations with taxonomy-adaptive neural networks","year":2025,"lang":"en","type":"article","venue":"Microbiome","topic":"Gut microbiota and health","field":"Biochemistry, Genetics and Molecular Biology","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Microbiome; Biology; Metagenomics; Interpretability; Trait; Human Microbiome Project; Human microbiome; Computational biology; Identification (biology); Taxonomic rank; Taxon; Microbial ecology; Ecology; Evolutionary biology; Artificial intelligence; Bioinformatics; Computer science; Genetics; Gene","routes":{"ca_aff":true,"ca_fund":false,"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":[0.001244078,0.0007726519,0.0005256743,0.000681041,0.0004568314,0.0007664702,0.001406459,0.00151119,0.002285372],"category_scores_gemma":[0.004814253,0.0005037345,0.00097277,0.000782489,0.0006020442,0.0007990451,0.0008584829,0.00165598,0.0003192651],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001363066,"about_ca_system_score_gemma":0.0009859885,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03214661,"about_ca_topic_score_gemma":0.02646416,"domain_scores_codex":[0.9997068,0.0001117113,0.00001195536,0.00008586827,0.00003397317,0.00004970968],"domain_scores_gemma":[0.9984901,0.001094725,0.0001423372,0.00004887618,0.0001675824,0.00005635644],"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":[0.00004070088,0.00002282612,0.002571791,0.00001410685,0.00002446079,0.00002618577,0.00001585649,0.9870458,0.0002432801,0.001752447,0.0004212989,0.007821343],"study_design_scores_gemma":[0.000002261448,0.000003159299,0.0001391707,0.000001333027,0.000001433871,0.000002017286,0.000001447104,0.9987979,0.00002761952,0.0009832884,0.00003909066,0.000001325841],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3298067,0.001212808,0.6578352,0.001965107,0.0002006399,0.0001482183,0.002273825,0.0009533078,0.005604387],"genre_scores_gemma":[0.9264432,0.0003486015,0.06614266,0.0003709463,0.00008385679,0.0003037261,0.001351573,0.00005143632,0.004904016],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03214661,"threshold_uncertainty_score":0.06391895,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01441524040319964,"score_gpt":0.240499791690801,"score_spread":0.2260845512876014,"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."}}