{"id":"W2130147841","doi":"10.1371/journal.pcbi.1000692","title":"Interpreting Metabolomic Profiles using Unbiased Pathway Models","year":2010,"lang":"en","type":"article","venue":"PLoS Computational Biology","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":55,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Human Genome Research Institute; National Institute of Diabetes and Digestive and Kidney Diseases; National Heart, Lung, and Blood Institute; National Institutes of Health; National Institute of Neurological Disorders and Stroke; American Heart Association; Canadian Institute for Advanced Research; Fondation Leducq","keywords":"Metabolite; Metabolomics; Biology; Metabolome; Metabolic network; Metabolic pathway; Transporter; Biochemistry; Computational biology; Bioinformatics; Metabolism; Gene","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00129358,0.001287285,0.000526907,0.001927422,0.0003797641,0.001340527,0.0006033723,0.0006201941,0.001440765],"category_scores_gemma":[0.003719938,0.000380564,0.001339414,0.001448659,0.0003178453,0.001022269,0.0008927956,0.0005988693,0.0003118327],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006105507,"about_ca_system_score_gemma":0.0009508052,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004489111,"about_ca_topic_score_gemma":0.004202228,"domain_scores_codex":[0.9995284,0.0002233119,0.00001967756,0.0001232611,0.00006669376,0.00003862922],"domain_scores_gemma":[0.9991093,0.0005908091,0.0001068318,0.00009116125,0.00007337197,0.00002848784],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001753969,0.00007473638,0.007695751,0.0001790152,0.0003317979,0.0002774906,0.0001103528,0.9339137,0.00982937,0.01563974,0.0006587527,0.03111398],"study_design_scores_gemma":[0.00001042407,0.00004338475,0.001487616,0.00001151639,0.00005502352,0.00003973421,0.00002472427,0.9731613,0.001183095,0.02291418,0.001055157,0.00001374597],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09374399,0.0005508123,0.8995497,0.0002899501,0.00003311803,0.0001981106,0.002705139,0.001068345,0.001860784],"genre_scores_gemma":[0.6860376,0.001457319,0.3041977,0.0001746755,0.00005528955,0.001017844,0.0049476,0.0002132047,0.001898702],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004489111,"threshold_uncertainty_score":0.008925974,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02407100103629794,"score_gpt":0.2694456954216913,"score_spread":0.2453746943853934,"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."}}