{"id":"W4416554645","doi":"10.1093/nar/gkaf1272","title":"MiMeDB 2.0: the Human Microbial Metabolome Database for 2026","year":2025,"lang":"en","type":"article","venue":"Nucleic Acids Research","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Genome Alberta; Canada Research Chairs; Canada Foundation for Innovation; National Institutes of Health; National Institute on Aging; Social Sciences and Humanities Research Council of Canada; Weston Family Foundation","keywords":"Metabolome; Metabolite; Human microbiome; Human Microbiome Project; Metabolomics; Microbiome; Identification (biology); Metagenomics; Microbial metabolism","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":[0.001611802,0.001719254,0.002239813,0.003924637,0.0005733892,0.002838536,0.002092591,0.001566932,0.05044862],"category_scores_gemma":[0.004717612,0.0009273625,0.001346993,0.005702508,0.0002414912,0.002278861,0.003956999,0.001430189,0.05282401],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005721497,"about_ca_system_score_gemma":0.002241529,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002803176,"about_ca_topic_score_gemma":0.003970195,"domain_scores_codex":[0.9992901,0.0001321071,0.0001244386,0.000157116,0.0001936452,0.0001024317],"domain_scores_gemma":[0.9989648,0.00019644,0.0001825537,0.0001472021,0.0002337225,0.0002752987],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00254141,0.00009747053,0.004605381,0.005273184,0.0003969108,0.0005253482,0.000188197,0.000808426,0.009395559,0.003441072,0.8848757,0.08785135],"study_design_scores_gemma":[0.0004302727,0.0001058712,0.007724599,0.0008571937,0.0001819903,0.0004551759,0.00008733104,0.001207435,0.003343917,0.00460145,0.9809077,0.00009702158],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"software","genre_scores_codex":[0.002587831,0.00559976,0.005914883,0.0006480455,0.0001731676,0.000163965,0.9711053,0.007654475,0.006152544],"genre_scores_gemma":[0.004023205,0.002581377,0.01277662,0.0006345658,0.00006607668,0.0003134386,0.9768335,0.0009663893,0.001804792],"genre_candidate":"software","genre_consensus":null,"teacher_disagreement_score":0.05044862,"threshold_uncertainty_score":0.1687675,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0440663661705129,"score_gpt":0.3771924515578407,"score_spread":0.3331260853873278,"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."}}