{"id":"W2767683865","doi":"10.1093/nar/gkx1089","title":"HMDB 4.0: the human metabolome database for 2018","year":2017,"lang":"en","type":"article","venue":"Nucleic Acids Research","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":3554,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Genome Alberta; Alberta Innovates; Alberta Innovates - Health Solutions; Canadian Institutes of Health Research; Genome Canada; World Health Organization","keywords":"Metabolome; Metabolomics; Metabolite; Biology; Identification (biology); Database; Computer science; Computational biology; 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":[0.001995767,0.001528123,0.001793693,0.004552646,0.0006718254,0.00326733,0.002977831,0.001553979,0.07628317],"category_scores_gemma":[0.006795799,0.0009412635,0.001209607,0.006405089,0.0002677939,0.00322569,0.003430519,0.00166887,0.06491581],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000905698,"about_ca_system_score_gemma":0.003165485,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005860414,"about_ca_topic_score_gemma":0.006229036,"domain_scores_codex":[0.9991036,0.0001725779,0.0001728055,0.00019095,0.0002456938,0.0001143456],"domain_scores_gemma":[0.9983854,0.0003199153,0.0002105976,0.0003258821,0.0004367868,0.0003214594],"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":[0.0005762203,0.00003512269,0.001463394,0.001700733,0.0001254242,0.0001498906,0.0000848901,0.000408142,0.001314131,0.002432951,0.9384363,0.05327283],"study_design_scores_gemma":[0.0001788786,0.00004915118,0.00473571,0.0005665998,0.00008137169,0.000294055,0.00006517996,0.0007842676,0.001354084,0.006474643,0.9853483,0.00006775002],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00166603,0.006007333,0.009898185,0.001357195,0.0002958018,0.000237596,0.9518712,0.01571981,0.01294687],"genre_scores_gemma":[0.004196196,0.003304811,0.01725679,0.001012305,0.000139941,0.0004821956,0.9664087,0.002348959,0.004850207],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.07628317,"threshold_uncertainty_score":0.2551926,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09984199163691135,"score_gpt":0.4088378265351218,"score_spread":0.3089958348982105,"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."}}