{"id":"W2513955315","doi":"10.1007/s11306-016-1094-6","title":"Metabolomics enables precision medicine: “A White Paper, Community Perspective”","year":2016,"lang":"en","type":"article","venue":"Metabolomics","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":615,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"National Institute of General Medical Sciences; National Institute on Aging; National Institute of Diabetes and Digestive and Kidney Diseases; National Heart, Lung, and Blood Institute; Medical Research Council","keywords":"Metabolomics; Precision medicine; Computational biology; Perspective (graphical); Molecular medicine; Biology; Bioinformatics; Computer science; Genetics; Cancer; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"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":[0.04303625,0.001832458,0.00215346,0.003507596,0.003289984,0.01654978,0.004483491,0.02781203,0.014119],"category_scores_gemma":[0.06294607,0.0008279146,0.002017359,0.002799533,0.0137027,0.02065009,0.007719706,0.03925779,0.01028803],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00581719,"about_ca_system_score_gemma":0.01644754,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003609462,"about_ca_topic_score_gemma":0.003178464,"domain_scores_codex":[0.9814538,0.006190059,0.001685542,0.002762626,0.006464942,0.001442958],"domain_scores_gemma":[0.8948741,0.04178028,0.003542049,0.004635205,0.0356712,0.01949723],"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":[0.00004690282,0.00005185173,0.0001937725,0.0003756281,0.000030742,0.0001191711,0.0001279882,0.0001098761,0.000183875,0.02853358,0.9080887,0.06213797],"study_design_scores_gemma":[0.00002079749,0.00006498015,0.0003228942,0.001471829,0.0000239365,0.000253833,0.0002091932,0.0001368603,0.0002285912,0.03190522,0.9653122,0.00004957725],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"commentary","genre_scores_codex":[0.0001171354,0.0656096,0.001758538,0.8564361,0.07160063,0.00002166148,0.0001543731,0.0001328708,0.004169183],"genre_scores_gemma":[0.005222125,0.1445426,0.006076084,0.6481735,0.1836489,0.00009525468,0.0003229651,0.0002292423,0.01168934],"genre_candidate":"commentary","genre_consensus":"commentary","teacher_disagreement_score":0.04303625,"threshold_uncertainty_score":0.2276002,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01796202440805935,"score_gpt":0.2701658773568726,"score_spread":0.2522038529488133,"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."}}