{"id":"W4206447491","doi":"10.1093/nar/gkab1062","title":"HMDB 5.0: the Human Metabolome Database for 2022","year":2021,"lang":"en","type":"article","venue":"Nucleic Acids Research","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2449,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Genome Alberta; Canada Foundation for Innovation; Alberta Machine Intelligence Institute","keywords":"Metabolome; Metabolomics; Identification (biology); Computer science; Metabolite; Biology; Interface (matter); Data mining; Database; Bioinformatics; Biochemistry","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":[0.002015252,0.001481749,0.001734701,0.004667419,0.0005762113,0.003071814,0.00244324,0.001432568,0.07390227],"category_scores_gemma":[0.006367651,0.0008761896,0.001229124,0.006018944,0.0002376167,0.002868057,0.003157741,0.001465741,0.06570721],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009050179,"about_ca_system_score_gemma":0.003019161,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007236134,"about_ca_topic_score_gemma":0.008439615,"domain_scores_codex":[0.99925,0.0001613713,0.0001461327,0.0001376497,0.0002025239,0.0001022346],"domain_scores_gemma":[0.9981434,0.0003236823,0.0002438794,0.0002782899,0.0005869077,0.0004238168],"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":[0.0004497103,0.00002992209,0.00166166,0.001273496,0.0001151417,0.0001038208,0.00005245868,0.0002806558,0.0007473548,0.001538581,0.948342,0.04540526],"study_design_scores_gemma":[0.0001901633,0.00005262796,0.006828452,0.0006955446,0.0001043972,0.0002429425,0.0000791682,0.0007463812,0.001058984,0.005163023,0.9847683,0.00007010958],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.001676777,0.005202762,0.007080475,0.001808864,0.0002874997,0.000200463,0.9619507,0.01083043,0.01096203],"genre_scores_gemma":[0.004293776,0.003045755,0.01580802,0.001352043,0.0001551195,0.0003438767,0.9696594,0.001451829,0.0038901],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.07390227,"threshold_uncertainty_score":0.2472277,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06413457580120722,"score_gpt":0.3800104074604811,"score_spread":0.3158758316592739,"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."}}