{"id":"W4304117487","doi":"10.1093/nar/gkac868","title":"MiMeDB: the Human Microbial Metabolome Database","year":2022,"lang":"en","type":"article","venue":"Nucleic Acids Research","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":111,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"National Institute on Aging; Natural Sciences and Engineering Research Council of Canada; Genome Canada; Canadian Institutes of Health Research; National Institutes of Health; Canada Foundation for Innovation","keywords":"Metabolome; Exposome; Biology; Microbiome; Human Microbiome Project; Metagenomics; Human microbiome; Human health; Genome; Computational biology; Microbial metabolism; Microbial ecology; Metabolomics; Bacteria; Genetics; Bioinformatics; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001326724,0.001683088,0.002261524,0.004618185,0.0006765234,0.00278308,0.002723357,0.001689346,0.05985206],"category_scores_gemma":[0.004589895,0.0007705381,0.00133799,0.006592778,0.0002645472,0.002287104,0.003918496,0.001453535,0.04910117],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007093144,"about_ca_system_score_gemma":0.002258423,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003873044,"about_ca_topic_score_gemma":0.005049015,"domain_scores_codex":[0.9992762,0.0001433052,0.0001473936,0.0001689798,0.0001699176,0.00009426803],"domain_scores_gemma":[0.9989552,0.0002279772,0.0001650939,0.0001828977,0.0002205599,0.0002482858],"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":[0.001980795,0.0001268535,0.003371578,0.00816602,0.0004030933,0.0005796556,0.0002053538,0.0008993212,0.006495417,0.003972549,0.9071786,0.06662074],"study_design_scores_gemma":[0.0003486836,0.00008292299,0.006893876,0.0009808186,0.0001868953,0.0004986671,0.0001122894,0.0009732989,0.002782079,0.005706964,0.9813394,0.00009397499],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"software","genre_scores_codex":[0.001184723,0.002848266,0.00283257,0.0003117117,0.00006928443,0.0001106454,0.9842159,0.004334494,0.00409254],"genre_scores_gemma":[0.003361339,0.001972856,0.008470172,0.0003961899,0.00003553779,0.0002917156,0.983561,0.0006079075,0.001303227],"genre_candidate":"software","genre_consensus":null,"teacher_disagreement_score":0.05985206,"threshold_uncertainty_score":0.2002251,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04698035397063809,"score_gpt":0.3454578879005448,"score_spread":0.2984775339299067,"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."}}