{"id":"W2766295385","doi":"10.1093/nar/gkx1061","title":"PAMDB: a comprehensive Pseudomonas aeruginosa metabolome database","year":2017,"lang":"en","type":"article","venue":"Nucleic Acids Research","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":65,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"National Institute of General Medical Sciences; National Institute of Allergy and Infectious Diseases; Canadian Institutes of Health Research; National Institutes of Health; Genome Canada","keywords":"Metabolome; Biology; Database; Metabolomics; Pseudomonas aeruginosa; Organism; Metabolite; Computational biology; Metabolic pathway; Gene; Bacteria; Biochemistry; Bioinformatics; Genetics; Computer science","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":[0.0007461967,0.001903327,0.00164874,0.006936654,0.000866423,0.001629602,0.001550822,0.001226335,0.0152707],"category_scores_gemma":[0.001968679,0.0005469812,0.0009955728,0.007399977,0.000238406,0.001779184,0.002211643,0.0009419969,0.013354],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006882144,"about_ca_system_score_gemma":0.002383159,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003583037,"about_ca_topic_score_gemma":0.004086335,"domain_scores_codex":[0.9994239,0.00009772445,0.0001169078,0.0001368538,0.0001502142,0.00007442146],"domain_scores_gemma":[0.9993512,0.0001080636,0.0001377297,0.00009122559,0.0001694877,0.0001423038],"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":[0.005901677,0.0006421067,0.01919729,0.02585912,0.001052948,0.003560705,0.0007168603,0.004191162,0.2021917,0.004735418,0.4846523,0.2472988],"study_design_scores_gemma":[0.0005333658,0.0004416051,0.03555402,0.0009096743,0.000560363,0.002385471,0.000269221,0.005924004,0.02890732,0.004666516,0.9196162,0.0002322455],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.01956012,0.007445231,0.01010422,0.0003959706,0.0000722346,0.0004392209,0.9448001,0.01047385,0.00670904],"genre_scores_gemma":[0.01640067,0.003670211,0.01858055,0.0001876899,0.0000367018,0.0004010664,0.958932,0.0004576968,0.001333376],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.0152707,"threshold_uncertainty_score":0.05108559,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07653611038288945,"score_gpt":0.3768760278731253,"score_spread":0.3003399174902359,"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."}}