{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001766528,0.0001810723,0.000237892,0.00009520527,0.0001326572,0.0000194338,0.0001925187,0.0001583362,0.0000436112],"category_scores_gemma":[0.0001705233,0.0001624382,0.00009534093,0.00009529574,0.0001504924,0.000005604378,0.0001867162,0.0001746869,0.000009225578],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008201957,"about_ca_system_score_gemma":0.00009991123,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001229951,"about_ca_topic_score_gemma":0.00001107895,"domain_scores_codex":[0.9989008,0.00007794274,0.0002626813,0.0004125544,0.00008087997,0.0002651498],"domain_scores_gemma":[0.9994085,0.00006029691,0.0001284273,0.000183143,0.0001582616,0.00006141351],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00004180884,0.00006157701,0.001391094,0.000006424946,0.0001442905,7.836809e-7,0.00002498568,0.002683629,0.974053,0.02101161,0.00007051976,0.0005103257],"study_design_scores_gemma":[0.001505861,0.0004447551,0.002176232,0.00001923586,0.000105582,0.00006952723,0.0001163716,0.2496877,0.6618168,0.07368341,0.009398003,0.0009764765],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9682557,0.0003948894,0.02960372,0.0001449943,0.0003575575,0.0001711978,0.00008644976,0.0000254091,0.0009601003],"genre_scores_gemma":[0.9534252,0.00002333418,0.04557447,0.0003188608,0.0002667829,0.00002121514,0.0003093456,0.00002079508,0.00003998223],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3122361,"threshold_uncertainty_score":0.6624036,"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."}}