{"id":"W4224255375","doi":"10.1038/s41598-022-10415-5","title":"Clinical diagnosis of metabolic disorders using untargeted metabolomic profiling and disease-specific networks learned from profiling data","year":2022,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Metabolism and Genetic Disorders","field":"Biochemistry, Genetics and Molecular Biology","cited_by":41,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"National Center for Advancing Translational Sciences; U.S. National Library of Medicine; National Institute of Diabetes and Digestive and Kidney Diseases; National Institute of Neurological Disorders and Stroke; National Institute of General Medical Sciences; National Human Genome Research Institute; Canadian Institutes of Health Research; National Institutes of Health","keywords":"Metabolomics; Methylmalonic acidemia; Metabolome; Ornithine transcarbamylase deficiency; Maple syrup urine disease; Bioinformatics; Metabolite; Computational biology; Medicine; Urea cycle; Biology; Endocrinology; Genetics; Amino acid","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":[0.001007472,0.0009218459,0.0005185508,0.001722616,0.000249578,0.0009142343,0.0005354094,0.0005421558,0.0008299692],"category_scores_gemma":[0.004658691,0.0002655851,0.0007097087,0.0008975185,0.0003394665,0.0006772527,0.0008728099,0.0006873181,0.0002457661],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008405573,"about_ca_system_score_gemma":0.0005887799,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004400104,"about_ca_topic_score_gemma":0.006219975,"domain_scores_codex":[0.9993259,0.0002182982,0.00005207475,0.0002807734,0.00008304397,0.00003992972],"domain_scores_gemma":[0.9982274,0.000953517,0.0003652088,0.0001835128,0.0001798155,0.00009057414],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001710514,0.0003878819,0.3641279,0.0004443806,0.001049831,0.001206619,0.0002666299,0.423354,0.03579437,0.003119554,0.00305327,0.1654851],"study_design_scores_gemma":[0.00003619395,0.0001286043,0.03717368,0.0000256893,0.00009791239,0.0003380399,0.00004793498,0.9490395,0.007002524,0.005114386,0.0009647667,0.00003085137],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6785176,0.001016341,0.3092372,0.0008777304,0.00007572197,0.0002105793,0.006211594,0.001262681,0.002590493],"genre_scores_gemma":[0.9396076,0.0002312608,0.05623665,0.000128748,0.00002814806,0.0001009984,0.00324208,0.00004267276,0.000381894],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004400104,"threshold_uncertainty_score":0.008749008,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05985132600460833,"score_gpt":0.3115624048904991,"score_spread":0.2517110788858907,"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."}}