{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T21:25:50Z","timestamp":1772832350117,"version":"3.50.1"},"reference-count":309,"publisher":"Oxford University Press (OUP)","issue":"5","license":[{"start":{"date-parts":[[2021,4,19]],"date-time":"2021-04-19T00:00:00Z","timestamp":1618790400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["81872798"],"award-info":[{"award-number":["81872798"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U1909208"],"award-info":[{"award-number":["U1909208"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004731","name":"Natural Science Foundation of Zhejiang Province","doi-asserted-by":"publisher","award":["LR21H300001"],"award-info":[{"award-number":["LR21H300001"]}],"id":[{"id":"10.13039\/501100004731","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2018YFC0910500"],"award-info":[{"award-number":["2018YFC0910500"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Fundamental Research Fund for Central Universities","award":["2018QNA7023"],"award-info":[{"award-number":["2018QNA7023"]}]},{"name":"Key Research and Development Program of Zhejiang Province","award":["2020C03010"],"award-info":[{"award-number":["2020C03010"]}]},{"name":"Information Technology Center, Zhejiang University"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,9,2]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Individual variations in drug efficacy, side effects and adverse drug reactions are still challenging that cannot be ignored in drug research and development. The aim of pharmacometabonomics is to better understand the pharmacokinetic properties of drugs and monitor the drug effects on specific metabolic pathways. Here, we systematically reviewed the recent technological advances in pharmacometabonomics for better understanding the pathophysiological mechanisms of diseases as well as the metabolic effects of drugs on bodies. First, the advantages and disadvantages of all mainstream analytical techniques were compared. Second, many data processing strategies including filtering, missing value imputation, quality control-based correction, transformation, normalization together with the methods implemented in each step were discussed. Third, various feature selection and feature extraction algorithms commonly applied in pharmacometabonomics were described. Finally, the databases that facilitate current pharmacometabonomics were collected and discussed. All in all, this review provided guidance for researchers engaged in pharmacometabonomics and metabolomics, and it would promote the wide application of metabolomics in drug research and personalized medicine.<\/jats:p>","DOI":"10.1093\/bib\/bbab138","type":"journal-article","created":{"date-parts":[[2021,4,16]],"date-time":"2021-04-16T18:51:13Z","timestamp":1618599073000},"source":"Crossref","is-referenced-by-count":60,"title":["Pharmacometabonomics: data processing and statistical analysis"],"prefix":"10.1093","volume":"22","author":[{"given":"Jianbo","family":"Fu","sequence":"first","affiliation":[{"name":"College of Pharmaceutical Sciences in Zhejiang University, China"}]},{"given":"Ying","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Pharmaceutical Sciences in Zhejiang University, China"}]},{"given":"Jin","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Pharmaceutical Sciences in Zhejiang University, China"}]},{"given":"Xichen","family":"Lian","sequence":"additional","affiliation":[{"name":"College of Pharmaceutical Sciences in Zhejiang University, China"}]},{"given":"Jing","family":"Tang","sequence":"additional","affiliation":[{"name":"Department of Bioinformatics in Chongqing Medical University, China"}]},{"given":"Feng","family":"Zhu","sequence":"additional","affiliation":[{"name":"College of Pharmaceutical Sciences in Zhejiang University, China"}]}],"member":"286","published-online":{"date-parts":[[2021,4,19]]},"reference":[{"key":"2021090813432244500_ref1","doi-asserted-by":"crossref","first-page":"16065","DOI":"10.1038\/nrdp.2016.65","article-title":"Major depressive disorder","volume":"2","author":"Otte","year":"2016","journal-title":"Nat Rev Dis Primers"},{"key":"2021090813432244500_ref2","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1016\/j.ajp.2017.01.025","article-title":"The neurobiology of depression: an integrated view","volume":"27","author":"Dean","year":"2017","journal-title":"Asian J Psychiatr"},{"key":"2021090813432244500_ref3","doi-asserted-by":"crossref","first-page":"460","DOI":"10.2174\/1574893613666181112130346","article-title":"In-silico identification of drug lead molecule against pesticide exposed-neurodevelopmental disorders through network-based computational model approach","volume":"14","author":"Srivastava","year":"2019","journal-title":"Curr Bioinform"},{"key":"2021090813432244500_ref4","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1038\/s41572-019-0106-z","article-title":"Atherosclerosis","volume":"5","author":"Libby","year":"2019","journal-title":"Nat Rev Dis Primers"},{"key":"2021090813432244500_ref5","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1152\/physrev.00045.2011","article-title":"Mechanisms of diabetic complications","volume":"93","author":"Forbes","year":"2013","journal-title":"Physiol Rev"},{"key":"2021090813432244500_ref6","doi-asserted-by":"crossref","first-page":"1492","DOI":"10.1021\/acschemneuro.8b00059","article-title":"Exploring the binding mechanism of metabotropic glutamate receptor 5 negative allosteric modulators in clinical trials by molecular dynamics simulations","volume":"9","author":"Fu","year":"2018","journal-title":"ACS Chem Nerosci"},{"key":"2021090813432244500_ref7","doi-asserted-by":"crossref","first-page":"592","DOI":"10.1007\/s13238-014-0082-8","article-title":"Regulation of the pentose phosphate pathway in cancer","volume":"5","author":"Jiang","year":"2014","journal-title":"Protein Cell"},{"key":"2021090813432244500_ref8","doi-asserted-by":"crossref","first-page":"3411","DOI":"10.1016\/j.jmb.2020.01.027","article-title":"SSizer: determining the sample sufficiency for comparative biological study","volume":"432","author":"Li","year":"2020","journal-title":"J Mol Biol"},{"key":"2021090813432244500_ref9","doi-asserted-by":"crossref","first-page":"398","DOI":"10.1093\/bioinformatics\/btx622","article-title":"Tumor origin detection with tissue-specific miRNA and DNA methylation markers","volume":"34","author":"Tang","year":"2018","journal-title":"Bioinformatics"},{"key":"2021090813432244500_ref10","doi-asserted-by":"crossref","first-page":"640","DOI":"10.2174\/157489361407190917161654","article-title":"LncRNA in tumorigenesis microenvironment","volume":"14","author":"Ji","year":"2019","journal-title":"Curr Bioinform"},{"key":"2021090813432244500_ref11","doi-asserted-by":"crossref","first-page":"472","DOI":"10.2174\/1574893614666190902152727","article-title":"The human oncobiome database: a database of cancer microbiome datasets","volume":"15","author":"Nadia","year":"2020","journal-title":"Curr Bioinform"},{"key":"2021090813432244500_ref12","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1093\/aje\/kwv040","article-title":"A meta-regression method for studying etiological heterogeneity across disease subtypes classified by multiple biomarkers","volume":"182","author":"Wang","year":"2015","journal-title":"Am J Epidemiol"},{"key":"2021090813432244500_ref13","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1007\/s11910-017-0738-x","article-title":"Subtypes of parkinson's disease: what do they tell us about disease progression?","volume":"17","author":"Fereshtehnejad","year":"2017","journal-title":"Curr Neurol Neurosci Rep"},{"key":"2021090813432244500_ref14","doi-asserted-by":"crossref","first-page":"709","DOI":"10.2174\/1574893614666190220114644","article-title":"Predicting drug side effects with compact integration of heterogeneous networks","volume":"14","author":"Zhao","year":"2019","journal-title":"Curr Bioinform"},{"key":"2021090813432244500_ref15","doi-asserted-by":"crossref","first-page":"1173","DOI":"10.3390\/molecules22071173","article-title":"Novel applications of metabolomics in personalized medicine: a mini-review","volume":"22","author":"Li","year":"2017","journal-title":"Molecules"},{"key":"2021090813432244500_ref16","doi-asserted-by":"crossref","first-page":"432","DOI":"10.1007\/s13238-018-0547-2","article-title":"Pharmacomicrobiomics: a novel route towards personalized medicine?","volume":"9","author":"Doestzada","year":"2018","journal-title":"Protein Cell"},{"key":"2021090813432244500_ref17","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.ymeth.2019.02.009","article-title":"Deep-Resp-Forest: a deep forest model to predict anti-cancer drug response","volume":"166","author":"Su","year":"2019","journal-title":"Methods"},{"key":"2021090813432244500_ref18","first-page":"232","article-title":"MMEASE: online meta-analysis of metabolomic data by enhanced metabolite annotation, marker selection and enrichment analysis","volume":"104023","author":"Yang","year":"2021","journal-title":"J Proteomics"},{"key":"2021090813432244500_ref19","doi-asserted-by":"crossref","first-page":"451","DOI":"10.1038\/nrm.2016.25","article-title":"Metabolomics: beyond biomarkers and towards mechanisms","volume":"17","author":"Johnson","year":"2016","journal-title":"Nat Rev Mol Cell Biol"},{"key":"2021090813432244500_ref20","doi-asserted-by":"crossref","first-page":"101069","DOI":"10.1016\/j.arr.2020.101069","article-title":"Discovery of new epigenomics-based biomarkers and the early diagnosis of neurodegenerative diseases","volume":"61","author":"Lee","year":"2020","journal-title":"Ageing Res Rev"},{"key":"2021090813432244500_ref21","doi-asserted-by":"crossref","first-page":"473","DOI":"10.1038\/nrd.2016.32","article-title":"Emerging applications of metabolomics in drug discovery and precision medicine","volume":"15","author":"Wishart","year":"2016","journal-title":"Nat Rev Drug Discov"},{"key":"2021090813432244500_ref22","doi-asserted-by":"crossref","first-page":"1049","DOI":"10.2217\/pgs-2019-0089","article-title":"miRNAs in drug response variability: potential utility as biomarkers for personalized medicine","volume":"20","author":"Latini","year":"2019","journal-title":"Pharmacogenomics"},{"key":"2021090813432244500_ref23","first-page":"29","article-title":"Predictive biomarkers for linking disease pathology and drug effect","volume":"23","author":"Mayer","year":"2017","journal-title":"Curr Pharm Des"},{"key":"2021090813432244500_ref24","doi-asserted-by":"crossref","first-page":"103660","DOI":"10.1016\/j.compbiomed.2020.103660","article-title":"Exploration of the correlation between GPCRs and drugs based on a learning to rank algorithm","volume":"119","author":"Ru","year":"2020","journal-title":"Comput Biol Med"},{"key":"2021090813432244500_ref25","first-page":"1\u201314","article-title":"Subtype-selective mechanisms of negative allosteric modulators binding to group I metabotropic glutamate receptors","volume":"0","author":"Fu","year":"2020","journal-title":"Acta Pharmacol Sin"},{"key":"2021090813432244500_ref26","doi-asserted-by":"crossref","first-page":"477","DOI":"10.1002\/cpt.1532","article-title":"The age of omics-driven precision medicine","volume":"106","author":"McColl","year":"2019","journal-title":"Clin Pharmacol Ther"},{"key":"2021090813432244500_ref27","doi-asserted-by":"crossref","first-page":"41","DOI":"10.2174\/1574893614666190409112025","article-title":"Integration and querying of heterogeneous omics semantic annotations for biomedical and biomolecular knowledge discovery","volume":"15","author":"Irshad","year":"2020","journal-title":"Curr Bioinform"},{"key":"2021090813432244500_ref28","doi-asserted-by":"crossref","first-page":"1825","DOI":"10.1093\/bib\/bbz120","article-title":"Convolutional neural network-based annotation of bacterial type IV secretion system effectors with enhanced accuracy and reduced false discovery","volume":"21","author":"Hong","year":"2020","journal-title":"Brief Bioinform"},{"key":"2021090813432244500_ref29","doi-asserted-by":"crossref","first-page":"234","DOI":"10.1093\/oxfordjournals.bmb.a069915","article-title":"Pharmacogenetics","volume":"17","author":"Evans","year":"1961","journal-title":"Br Med Bull"},{"key":"2021090813432244500_ref30","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1002\/wsbm.42","article-title":"Pharmacogenomics: a systems approach","volume":"2","author":"Wang","year":"2010","journal-title":"Wiley Interdiscip Rev Syst Biol Med"},{"key":"2021090813432244500_ref31","doi-asserted-by":"crossref","first-page":"1253","DOI":"10.1111\/cns.13051","article-title":"Identification of novel immune-relevant drug target genes for alzheimer's disease by combining ontology inference with network analysis","volume":"24","author":"Han","year":"2018","journal-title":"CNS Neurosci Ther"},{"key":"2021090813432244500_ref32","doi-asserted-by":"crossref","first-page":"1073","DOI":"10.1038\/nature04648","article-title":"Pharmaco-metabonomic phenotyping and personalized drug treatment","volume":"440","author":"Clayton","year":"2006","journal-title":"Nature"},{"key":"2021090813432244500_ref33","doi-asserted-by":"crossref","first-page":"A410","DOI":"10.1289\/ehp.112-a410","article-title":"Metabolomics: what's happening downstream of DNA","volume":"112","author":"Schmidt","year":"2004","journal-title":"Environ Health Perspect"},{"key":"2021090813432244500_ref34","doi-asserted-by":"crossref","first-page":"653","DOI":"10.1146\/annurev.pharmtox.48.113006.094715","article-title":"Metabolomics: a global biochemical approach to drug response and disease","volume":"48","author":"Kaddurah-Daouk","year":"2008","journal-title":"Annu Rev Pharmacol Toxicol"},{"key":"2021090813432244500_ref35","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1038\/clpt.2013.217","article-title":"Pharmacometabolomics: implications for clinical pharmacology and systems pharmacology","volume":"95","author":"Kaddurah-Daouk","year":"2014","journal-title":"Clin Pharmacol Ther"},{"key":"2021090813432244500_ref36","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1007\/s11306-010-0207-x","article-title":"Lipidomic analysis of variation in response to simvastatin in the cholesterol and pharmacogenetics study","volume":"6","author":"Kaddurah-Daouk","year":"2010","journal-title":"Metabolomics"},{"key":"2021090813432244500_ref37","doi-asserted-by":"crossref","first-page":"e25482","DOI":"10.1371\/journal.pone.0025482","article-title":"Enteric microbiome metabolites correlate with response to simvastatin treatment","volume":"6","author":"Kaddurah-Daouk","year":"2011","journal-title":"PLoS One"},{"key":"2021090813432244500_ref38","doi-asserted-by":"crossref","first-page":"e38386","DOI":"10.1371\/journal.pone.0038386","article-title":"Metabolomics reveals amino acids contribute to variation in response to simvastatin treatment","volume":"7","author":"Trupp","year":"2012","journal-title":"PLoS One"},{"key":"2021090813432244500_ref39","doi-asserted-by":"crossref","first-page":"649","DOI":"10.1093\/bib\/bby130","article-title":"Clinical trials, progression-speed differentiating features and swiftness rule of the innovative targets of first-in-class drugs","volume":"21","author":"Li","year":"2020","journal-title":"Brief Bioinform"},{"key":"2021090813432244500_ref40","doi-asserted-by":"crossref","first-page":"448","DOI":"10.3390\/ijms18020448","article-title":"Integrating pharmacoproteomics into early-phase clinical development: state-of-the-art, challenges, and recommendations","volume":"18","author":"Nandal","year":"2017","journal-title":"Int J Mol Sci"},{"key":"2021090813432244500_ref41","doi-asserted-by":"crossref","first-page":"743","DOI":"10.1038\/nprot.2011.319","article-title":"Web-based inference of biological patterns, functions and pathways from metabolomic data using MetaboAnalyst","volume":"6","author":"Xia","year":"2011","journal-title":"Nat Protoc"},{"key":"2021090813432244500_ref42","doi-asserted-by":"crossref","first-page":"40","DOI":"10.3390\/metabo6040040","article-title":"A conversation on data mining strategies in LC\u2013MS untargeted metabolomics: pre-processing and pre-treatment steps","volume":"6","author":"Tugizimana","year":"2016","journal-title":"Metabolites"},{"key":"2021090813432244500_ref43","doi-asserted-by":"crossref","first-page":"1054","DOI":"10.1111\/cns.13196","article-title":"Identification of the gene signature reflecting schizophrenia's etiology by constructing artificial intelligence-based method of enhanced reproducibility","volume":"25","author":"Yang","year":"2019","journal-title":"CNS Neurosci Ther"},{"key":"2021090813432244500_ref44","doi-asserted-by":"crossref","first-page":"611","DOI":"10.1016\/j.csbj.2019.04.009","article-title":"Accounting for biological variation with linear mixed-effects modelling improves the quality of clinical metabolomics data","volume":"17","author":"Wanichthanarak","year":"2019","journal-title":"Comput Struct Biotechnol J"},{"key":"2021090813432244500_ref45","doi-asserted-by":"crossref","first-page":"1060","DOI":"10.1038\/nprot.2011.335","article-title":"Procedures for large-scale metabolic profiling of serum and plasma using gas chromatography and liquid chromatography coupled to mass spectrometry","volume":"6","author":"Dunn","year":"2011","journal-title":"Nat Protoc"},{"key":"2021090813432244500_ref46","doi-asserted-by":"crossref","first-page":"1493","DOI":"10.1093\/bioinformatics\/btu813","article-title":"Workflow4Metabolomics: a collaborative research infrastructure for computational metabolomics","volume":"31","author":"Giacomoni","year":"2015","journal-title":"Bioinformatics"},{"key":"2021090813432244500_ref47","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1016\/j.jprot.2015.01.019","article-title":"Enhancing metabolomics research through data mining","volume":"127","author":"Mart\u00ednez-Arranz","year":"2015","journal-title":"J Proteomics"},{"key":"2021090813432244500_ref48","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1146\/annurev-biochem-061516-044952","article-title":"Metabolite measurement: pitfalls to avoid and practices to follow","volume":"86","author":"Lu","year":"2017","journal-title":"Annu Rev Biochem"},{"key":"2021090813432244500_ref49","doi-asserted-by":"crossref","first-page":"D1042","DOI":"10.1093\/nar\/gkz779","article-title":"VARIDT 1.0: variability of drug transporter database","volume":"48","author":"Yin","year":"2020","journal-title":"Nucleic Acids Res"},{"key":"2021090813432244500_ref50","doi-asserted-by":"crossref","first-page":"348","DOI":"10.1016\/j.cmet.2007.10.005","article-title":"Metabolomics","volume":"6","author":"Idle","year":"2007","journal-title":"Cell Metab"},{"key":"2021090813432244500_ref51","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1007\/s40256-019-00364-2","article-title":"Plasma metabolites as predictors of warfarin outcome in atrial fibrillation","volume":"20","author":"Bawadikji","year":"2020","journal-title":"Am J Cardiovasc Drugs"},{"key":"2021090813432244500_ref52","doi-asserted-by":"crossref","first-page":"18305","DOI":"10.1039\/D0RA02406F","article-title":"A targeted neurotransmitter quantification and nontargeted metabolic profiling method for pharmacometabolomics analysis of olanzapine by using UPLC-HRMS","volume":"10","author":"Liu","year":"2020","journal-title":"RSC Adv"},{"key":"2021090813432244500_ref53","doi-asserted-by":"crossref","first-page":"2781","DOI":"10.1038\/s41596-018-0064-z","article-title":"A computational framework to integrate high-throughput '-omics' datasets for the identification of potential mechanistic links","volume":"13","author":"Pedersen","year":"2018","journal-title":"Nat Protoc"},{"key":"2021090813432244500_ref54","first-page":"87","article-title":"Machine learning and integrative analysis of biomedical big data","volume":"10","author":"Mirza","year":"2019","journal-title":"Gen"},{"key":"2021090813432244500_ref55","doi-asserted-by":"crossref","first-page":"1378","DOI":"10.1093\/bib\/bbz061","article-title":"A critical assessment of the feature selection methods used for biomarker discovery in current metaproteomics studies","volume":"21","author":"Tang","year":"2020","journal-title":"Brief Bioinform"},{"key":"2021090813432244500_ref56","doi-asserted-by":"crossref","first-page":"3302","DOI":"10.1021\/acs.analchem.8b04310","article-title":"mzTab-M: a data standard for sharing quantitative results in mass spectrometry metabolomics","volume":"91","author":"Hoffmann","year":"2019","journal-title":"Anal Chem"},{"key":"2021090813432244500_ref57","doi-asserted-by":"crossref","first-page":"334","DOI":"10.1186\/s12859-019-2871-9","article-title":"Filtering procedures for untargeted LC\u2013MS metabolomics data","volume":"20","author":"Schiffman","year":"2019","journal-title":"BMC Bioinformatics"},{"key":"2021090813432244500_ref58","first-page":"312","article-title":"Effects of imputation on correlation: implications for analysis of mass spectrometry data from multiple biological matrices","volume":"18","author":"Taylor","year":"2017","journal-title":"Brief Bioinform"},{"key":"2021090813432244500_ref59","doi-asserted-by":"crossref","first-page":"1023","DOI":"10.1093\/bib\/bbz036","article-title":"Genome-wide identification and analysis of the eQTL lncRNAs in multiple sclerosis based on RNA-seq data","volume":"21","author":"Han","year":"2020","journal-title":"Brief Bioinform"},{"key":"2021090813432244500_ref60","doi-asserted-by":"crossref","first-page":"2155","DOI":"10.1093\/bioinformatics\/btu175","article-title":"Normalization of metabolomics data with applications to correlation maps","volume":"30","author":"Jauhiainen","year":"2014","journal-title":"Bioinformatics"},{"key":"2021090813432244500_ref61","doi-asserted-by":"crossref","first-page":"1058","DOI":"10.1093\/bib\/bbz049","article-title":"Consistent gene signature of schizophrenia identified by a novel feature selection strategy from comprehensive sets of transcriptomic data","volume":"21","author":"Yang","year":"2020","journal-title":"Brief Bioinform"},{"key":"2021090813432244500_ref62","doi-asserted-by":"crossref","first-page":"461","DOI":"10.1038\/nmeth.4260","article-title":"Systems biology guided by XCMS online metabolomics","volume":"14","author":"Huan","year":"2017","journal-title":"Nat Methods"},{"key":"2021090813432244500_ref63","doi-asserted-by":"crossref","first-page":"634","DOI":"10.1093\/bioinformatics\/btk039","article-title":"MZmine: toolbox for processing and visualization of mass spectrometry based molecular profile data","volume":"22","author":"Katajamaa","year":"2006","journal-title":"Bioinformatics"},{"key":"2021090813432244500_ref64","doi-asserted-by":"crossref","first-page":"W162","DOI":"10.1093\/nar\/gkx449","article-title":"NOREVA: normalization and evaluation of MS-based metabolomics data","volume":"45","author":"Li","year":"2017","journal-title":"Nucleic Acids Res"},{"key":"2021090813432244500_ref65","doi-asserted-by":"crossref","first-page":"W436","DOI":"10.1093\/nar\/gkaa258","article-title":"NOREVA: enhanced normalization and evaluation of time-course and multi-class metabolomic data","volume":"48","author":"Yang","year":"2020","journal-title":"Nucleic Acids Res"},{"key":"2021090813432244500_ref66","doi-asserted-by":"crossref","first-page":"W486","DOI":"10.1093\/nar\/gky310","article-title":"MetaboAnalyst 4.0: towards more transparent and integrative metabolomics analysis","volume":"46","author":"Chong","year":"2018","journal-title":"Nucleic Acids Res"},{"key":"2021090813432244500_ref67","doi-asserted-by":"crossref","first-page":"2012","DOI":"10.1016\/j.csbj.2020.07.009","article-title":"Computational advances of tumor marker selection and sample classification in cancer proteomics","volume":"18","author":"Tang","year":"2020","journal-title":"Comput Struct Biotechnol J"},{"key":"2021090813432244500_ref68","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1038\/clpt.2010.250","article-title":"Glycine and a glycine dehydrogenase (GLDC) SNP as citalopram\/escitalopram response biomarkers in depression: pharmacometabolomics-informed pharmacogenomics","volume":"89","author":"Ji","year":"2011","journal-title":"Clin Pharmacol Ther"},{"key":"2021090813432244500_ref69","doi-asserted-by":"crossref","first-page":"e97","DOI":"10.1371\/journal.pone.0000097","article-title":"A systems biology strategy reveals biological pathways and plasma biomarker candidates for potentially toxic statin-induced changes in muscle","volume":"1","author":"Laaksonen","year":"2006","journal-title":"PLoS One"},{"key":"2021090813432244500_ref70","doi-asserted-by":"crossref","first-page":"e26","DOI":"10.1038\/tp.2011.22","article-title":"Pretreatment metabotype as a predictor of response to sertraline or placebo in depressed outpatients: a proof of concept","volume":"1","author":"Kaddurah-Daouk","year":"2011","journal-title":"Transl Psychiatry"},{"key":"2021090813432244500_ref71","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1016\/j.aca.2015.02.012","article-title":"A tutorial review: metabolomics and partial least squares-discriminant analysis\u2013a marriage of convenience or a shotgun wedding","volume":"879","author":"Gromski","year":"2015","journal-title":"Anal Chim Acta"},{"key":"2021090813432244500_ref72","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.aca.2014.03.039","article-title":"A comparative investigation of modern feature selection and classification approaches for the analysis of mass spectrometry data","volume":"829","author":"Gromski","year":"2014","journal-title":"Anal Chim Acta"},{"key":"2021090813432244500_ref73","doi-asserted-by":"crossref","first-page":"150","DOI":"10.1007\/s11306-019-1612-4","article-title":"Broadhurst DI. A comparative evaluation of the generalised predictive ability of eight machine learning algorithms across ten clinical metabolomics data sets for binary classification","volume":"15","author":"Mendez","year":"2019","journal-title":"Metabolomics"},{"key":"2021090813432244500_ref74","doi-asserted-by":"crossref","first-page":"30","DOI":"10.3390\/metabo7020030","article-title":"Evaluation of classifier performance for multiclass phenotype discrimination in untargeted metabolomics","volume":"7","author":"Trainor","year":"2017","journal-title":"Metabolites"},{"key":"2021090813432244500_ref75","doi-asserted-by":"crossref","first-page":"D608","DOI":"10.1093\/nar\/gkx1089","article-title":"HMDB 4.0: the human metabolome database for 2018","volume":"46","author":"Wishart","year":"2018","journal-title":"Nucleic Acids Res"},{"issue":"D1","key":"2021090813432244500_ref76","doi-asserted-by":"crossref","first-page":"D354","DOI":"10.1093\/nar\/gkj102","article-title":"From genomics to chemical genomics: new developments in KEGG","volume":"34","author":"Kanehisa","year":"2006","journal-title":"Nucleic Acids Res"},{"key":"2021090813432244500_ref77","doi-asserted-by":"crossref","first-page":"826","DOI":"10.1038\/nbt.2348","article-title":"An accelerated workflow for untargeted metabolomics using the METLIN database","volume":"30","author":"Tautenhahn","year":"2012","journal-title":"Nat Biotechnol"},{"key":"2021090813432244500_ref78","doi-asserted-by":"crossref","first-page":"763","DOI":"10.1016\/j.tips.2019.08.004","article-title":"A unified conceptual framework for metabolic phenotyping in diagnosis and prognosis","volume":"40","author":"Everett","year":"2019","journal-title":"Trends Pharmacol Sci"},{"key":"2021090813432244500_ref79","doi-asserted-by":"crossref","first-page":"466","DOI":"10.1080\/03602532.2018.1559184","article-title":"Novel insights into the pharmacometabonomics of first-line tuberculosis drugs relating to metabolism, mechanism of action and drug-resistance","volume":"50","author":"Du Preez","year":"2018","journal-title":"Drug Metab Rev"},{"key":"2021090813432244500_ref80","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.pnmrs.2017.04.003","article-title":"NMR-based pharmacometabonomics: a new paradigm for personalised or precision medicine","volume":"102\u2013103","author":"Everett","year":"2017","journal-title":"Prog Nucl Magn Reson Spectrosc"},{"key":"2021090813432244500_ref81","doi-asserted-by":"crossref","first-page":"1075","DOI":"10.1007\/s11095-006-0025-z","article-title":"Metabonomics techniques and applications to pharmaceutical research & development","volume":"23","author":"Lindon","year":"2006","journal-title":"Pharm Res"},{"key":"2021090813432244500_ref82","doi-asserted-by":"crossref","first-page":"1128","DOI":"10.1021\/acschemneuro.7b00490","article-title":"What contributes to serotonin-norepinephrine reuptake inhibitors\u2019 dual-targeting mechanism? The key role of transmembrane domain 6 in human serotonin and norepinephrine transporters revealed by molecular dynamics simulation","volume":"9","author":"Xue","year":"2018","journal-title":"ACS Chem Nerosci"},{"key":"2021090813432244500_ref83","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1002\/jcph.1275","article-title":"Pharmacometabolomics reveals irinotecan mechanism of action in cancer patients","volume":"59","author":"Bao","year":"2019","journal-title":"J Clin Pharmacol"},{"key":"2021090813432244500_ref84","doi-asserted-by":"crossref","first-page":"9004","DOI":"10.1021\/acs.analchem.6b01481","article-title":"Development and application of ultra-performance liquid chromatography-TOF MS for precision large scale urinary metabolic phenotyping","volume":"88","author":"Lewis","year":"2016","journal-title":"Anal Chem"},{"key":"2021090813432244500_ref85","doi-asserted-by":"crossref","first-page":"9887","DOI":"10.1021\/ac5025039","article-title":"Precision high-throughput proton NMR spectroscopy of human urine, serum, and plasma for large-scale metabolic phenotyping","volume":"86","author":"Dona","year":"2014","journal-title":"Anal Chem"},{"key":"2021090813432244500_ref86","doi-asserted-by":"crossref","first-page":"849","DOI":"10.1002\/mrm.1910030605","article-title":"1H NMR studies of urine during fasting: excretion of ketone bodies and acetylcarnitine","volume":"3","author":"Bales","year":"1986","journal-title":"Magn Reson Med"},{"key":"2021090813432244500_ref87","doi-asserted-by":"crossref","first-page":"3006","DOI":"10.1074\/mcp.O113.030239","article-title":"An introduction to biological NMR spectroscopy","volume":"12","author":"Marion","year":"2013","journal-title":"Mol Cell Proteomics"},{"key":"2021090813432244500_ref88","doi-asserted-by":"crossref","first-page":"14728","DOI":"10.1073\/pnas.0904489106","article-title":"Pharmacometabonomic identification of a significant host-microbiome metabolic interaction affecting human drug metabolism","volume":"106","author":"Clayton","year":"2009","journal-title":"Proc Natl Acad Sci USA"},{"key":"2021090813432244500_ref89","doi-asserted-by":"crossref","first-page":"1448","DOI":"10.1002\/art.37921","article-title":"Metabolic profiling predicts response to anti-tumor necrosis factor \u03b1 therapy in patients with rheumatoid arthritis","volume":"65","author":"Kapoor","year":"2013","journal-title":"Arthritis Rheum"},{"key":"2021090813432244500_ref90","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1513\/AnnalsATS.201409-415OC","article-title":"Pharmacometabolomics of l-carnitine treatment response phenotypes in patients with septic shock","volume":"12","author":"Puskarich","year":"2015","journal-title":"Ann Am Thorac Soc"},{"key":"2021090813432244500_ref91","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1007\/s11306-016-0961-5","article-title":"Temporal characterization of serum metabolite signatures in lung cancer patients undergoing treatment","volume":"12","author":"Hao","year":"2016","journal-title":"Metabolomics"},{"key":"2021090813432244500_ref92","doi-asserted-by":"crossref","first-page":"6716","DOI":"10.1158\/1078-0432.CCR-09-1452","article-title":"Serum molecular signatures of weight change during early breast cancer chemotherapy","volume":"15","author":"Keun","year":"2009","journal-title":"Clin Cancer Res"},{"key":"2021090813432244500_ref93","doi-asserted-by":"crossref","first-page":"1437","DOI":"10.1093\/bib\/bbz081","article-title":"Protein functional annotation of simultaneously improved stability, accuracy and false discovery rate achieved by a sequence-based deep learning","volume":"21","author":"Hong","year":"2020","journal-title":"Brief Bioinform"},{"key":"2021090813432244500_ref94","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1038\/clpt.2009.240","article-title":"Use of pharmaco-metabonomics for early prediction of acetaminophen-induced hepatotoxicity in humans","volume":"88","author":"Winnike","year":"2010","journal-title":"Clin Pharmacol Ther"},{"key":"2021090813432244500_ref95","doi-asserted-by":"crossref","first-page":"3019","DOI":"10.1158\/1078-0432.CCR-10-2474","article-title":"Pharmacometabonomic profiling as a predictor of toxicity in patients with inoperable colorectal cancer treated with capecitabine","volume":"17","author":"Backshall","year":"2011","journal-title":"Clin Cancer Res"},{"key":"2021090813432244500_ref96","doi-asserted-by":"crossref","first-page":"4999","DOI":"10.1111\/bph.15234","article-title":"Databases for the targeted COVID-19 therapeutics","volume":"177","author":"Wang","year":"2020","journal-title":"Br J Pharmacol"},{"key":"2021090813432244500_ref97","doi-asserted-by":"crossref","first-page":"4630","DOI":"10.1021\/pr300430u","article-title":"Pharmacometabonomic characterization of xenobiotic and endogenous metabolic phenotypes that account for inter-individual variation in isoniazid-induced toxicological response","volume":"11","author":"Cunningham","year":"2012","journal-title":"J Proteome Res"},{"key":"2021090813432244500_ref98","doi-asserted-by":"crossref","first-page":"2427","DOI":"10.1021\/pr201161f","article-title":"Pharmacometabonomic investigation of dynamic metabolic phenotypes associated with variability in response to galactosamine hepatotoxicity","volume":"11","author":"Coen","year":"2012","journal-title":"J Proteome Res"},{"key":"2021090813432244500_ref99","first-page":"3","article-title":"Electrospray ionisation mass spectrometry: principles and clinical applications","volume":"24","author":"Ho","year":"2003","journal-title":"Clin Biochem Rev"},{"key":"2021090813432244500_ref100","doi-asserted-by":"crossref","first-page":"715","DOI":"10.1517\/17425251003713527","article-title":"Online electrochemistry\/mass spectrometry in drug metabolism studies: principles and applications","volume":"6","author":"Baumann","year":"2010","journal-title":"Expert Opin Drug Metab Toxicol"},{"key":"2021090813432244500_ref101","first-page":"19","article-title":"Principles and applications of liquid chromatography-mass spectrometry in clinical biochemistry","volume":"30","author":"Pitt","year":"2009","journal-title":"Clin Biochem Rev"},{"key":"2021090813432244500_ref102","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1002\/mas.21455","article-title":"Mass spectrometric based approaches in urine metabolomics and biomarker discovery","volume":"36","author":"Khamis","year":"2017","journal-title":"Mass Spectrom Rev"},{"key":"2021090813432244500_ref103","doi-asserted-by":"crossref","first-page":"6606","DOI":"10.1039\/C7CP07869B","article-title":"Computational identification of the binding mechanism of a triple reuptake inhibitor amitifadine for the treatment of major depressive disorder","volume":"20","author":"Xue","year":"2018","journal-title":"Phys Chem Chem Phys"},{"key":"2021090813432244500_ref104","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1007\/s11306-018-1449-2","article-title":"Review of recent developments in GC-MS approaches to metabolomics-based research","volume":"14","author":"Beale","year":"2018","journal-title":"Metabolomics"},{"key":"2021090813432244500_ref105","doi-asserted-by":"crossref","first-page":"7987","DOI":"10.1007\/s00216-018-1421-z","article-title":"Investigation of mycobacteria fatty acid profile using different ionization energies in GC-MS","volume":"410","author":"Beccaria","year":"2018","journal-title":"Anal Bioanal Chem"},{"key":"2021090813432244500_ref106","doi-asserted-by":"crossref","first-page":"2467","DOI":"10.1021\/acschemneuro.8b00729","article-title":"How does chirality determine the selective inhibition of histone deacetylase 6? A lesson from Trichostatin A enantiomers based on molecular dynamics","volume":"10","author":"Umebachi","year":"2019","journal-title":"ACS Chem Nerosci"},{"key":"2021090813432244500_ref107","doi-asserted-by":"crossref","first-page":"174","DOI":"10.1016\/j.jpba.2012.04.018","article-title":"LC\u2013MS determination of bioactive molecules based upon stable isotope-coded derivatization method","volume":"69","author":"Toyo'oka","year":"2012","journal-title":"J Pharm Biomed Anal"},{"key":"2021090813432244500_ref108","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.aca.2018.03.002","article-title":"Solid phase microextraction combined with thermal-desorption electrospray ionization mass spectrometry for high-throughput pharmacokinetics assays","volume":"1021","author":"Wang","year":"2018","journal-title":"Anal Chim Acta"},{"key":"2021090813432244500_ref109","doi-asserted-by":"crossref","first-page":"426","DOI":"10.1038\/clpt.2009.296","article-title":"An integrative approach for identifying a metabolic phenotype predictive of individualized pharmacokinetics of tacrolimus","volume":"87","author":"Phapale","year":"2010","journal-title":"Clin Pharmacol Ther"},{"key":"2021090813432244500_ref110","doi-asserted-by":"crossref","first-page":"3970","DOI":"10.1021\/acs.jproteome.5b00440","article-title":"A pharmacometabonomic approach to predicting metabolic phenotypes and pharmacokinetic parameters of atorvastatin in healthy volunteers","volume":"14","author":"Huang","year":"2015","journal-title":"J Proteome Res"},{"key":"2021090813432244500_ref111","doi-asserted-by":"crossref","first-page":"1860","DOI":"10.1093\/bib\/bbaa023","article-title":"The mechanistic, diagnostic and therapeutic novel nucleic acids for hepatocellular carcinoma emerging in past score years","volume":"22","author":"Zhang","year":"2021","journal-title":"Brief Bioinform"},{"key":"2021090813432244500_ref112","doi-asserted-by":"crossref","first-page":"e43389","DOI":"10.1371\/journal.pone.0043389","article-title":"Prediction of the pharmacokinetic parameters of triptolide in rats based on endogenous molecules in pre-dose baseline serum","volume":"7","author":"Liu","year":"2012","journal-title":"PLoS One"},{"key":"2021090813432244500_ref113","doi-asserted-by":"crossref","first-page":"601","DOI":"10.1038\/clpt.2013.128","article-title":"Evaluation of endogenous metabolic markers of hepatic CYP3A activity using metabolic profiling and midazolam clearance","volume":"94","author":"Shin","year":"2013","journal-title":"Clin Pharmacol Ther"},{"key":"2021090813432244500_ref114","doi-asserted-by":"crossref","first-page":"570","DOI":"10.1038\/clpt.2013.153","article-title":"Integration of pharmacometabolomic and pharmacogenomic approaches reveals novel insights into antiplatelet therapy","volume":"94","author":"Lewis","year":"2013","journal-title":"Clin Pharmacol Ther"},{"key":"2021090813432244500_ref115","doi-asserted-by":"crossref","first-page":"e125","DOI":"10.1038\/psp.2014.22","article-title":"Pharmacometabolomics reveals that serotonin is implicated in aspirin response variability","volume":"3","author":"Ellero-Simatos","year":"2014","journal-title":"CPT Pharmacometrics Syst Pharmacol"},{"key":"2021090813432244500_ref116","doi-asserted-by":"crossref","first-page":"1437","DOI":"10.2217\/pgs.14.84","article-title":"From pharmacogenetics to pharmacometabolomics: SAM modulates TPMT activity","volume":"15","author":"Karas-Ku\u017eeli\u010dki","year":"2014","journal-title":"Pharmacogenomics"},{"key":"2021090813432244500_ref117","doi-asserted-by":"crossref","first-page":"e621","DOI":"10.1038\/tp.2015.120","article-title":"Elevated baseline serum glutamate as a pharmacometabolomic biomarker for acamprosate treatment outcome in alcohol-dependent subjects","volume":"5","author":"Nam","year":"2015","journal-title":"Transl Psychiatry"},{"key":"2021090813432244500_ref118","doi-asserted-by":"crossref","first-page":"160","DOI":"10.1007\/s11306-016-1098-2","article-title":"Presence of arachidonoyl-carnitine is associated with adverse cardiometabolic responses in hypertensive patients treated with atenolol","volume":"12","author":"Weng","year":"2016","journal-title":"Metabolomics"},{"key":"2021090813432244500_ref119","doi-asserted-by":"crossref","first-page":"1364","DOI":"10.1021\/pr060513q","article-title":"Pharmacometabonomic phenotyping reveals different responses to xenobiotic intervention in rats","volume":"6","author":"Li","year":"2007","journal-title":"J Proteome Res"},{"key":"2021090813432244500_ref120","doi-asserted-by":"crossref","first-page":"2802","DOI":"10.1021\/acs.jproteome.6b00370","article-title":"Pharmacometabonomic prediction of busulfan clearance in hematopoetic cell transplant recipients","volume":"15","author":"Navarro","year":"2016","journal-title":"J Proteome Res"},{"key":"2021090813432244500_ref121","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1016\/j.phrs.2016.05.021","article-title":"Endogenous metabolites that are substrates of organic anion transporter's (OATs) predict methotrexate clearance","volume":"118","author":"Muhrez","year":"2017","journal-title":"Pharmacol Res"},{"key":"2021090813432244500_ref122","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1007\/s11306-015-0892-6","article-title":"A pharmacometabonomic approach using predose serum metabolite profiles reveals differences in lipid metabolism in survival and non-survival rats treated with lipopolysaccharide","volume":"12","author":"Dai","year":"2016","journal-title":"Metabolomics"},{"key":"2021090813432244500_ref123","doi-asserted-by":"crossref","first-page":"1753","DOI":"10.1021\/acs.jproteome.7b00014","article-title":"Branched-chain amino acids as predictors for individual differences of cisplatin nephrotoxicity in rats: a pharmacometabonomics study","volume":"16","author":"Zhang","year":"2017","journal-title":"J Proteome Res"},{"key":"2021090813432244500_ref124","doi-asserted-by":"crossref","first-page":"W127","DOI":"10.1093\/nar\/gks374","article-title":"MetaboAnalyst 2.0\u2013a comprehensive server for metabolomic data analysis","volume":"40","author":"Xia","year":"2012","journal-title":"Nucleic Acids Res"},{"key":"2021090813432244500_ref125","first-page":"1","article-title":"MetaFS: performance assessment of biomarker discovery in metaproteomics","volume":"00","author":"Tang","year":"2020","journal-title":"Brief Bioinform"},{"key":"2021090813432244500_ref126","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1007\/978-3-319-47656-8_6","article-title":"Preprocessing and pretreatment of metabolomics data for statistical analysis","volume":"965","author":"Karaman","year":"2017","journal-title":"Adv Exp Med Biol"},{"key":"2021090813432244500_ref127","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1016\/j.chroma.2015.12.007","article-title":"Sample normalization methods in quantitative metabolomics","volume":"1430","author":"Wu","year":"2016","journal-title":"J Chromatogr A"},{"key":"2021090813432244500_ref128","doi-asserted-by":"crossref","first-page":"W385","DOI":"10.1093\/nar\/gkaa332","article-title":"BIOMEX: an interactive workflow for (single cell) omics data interpretation and visualization","volume":"48","author":"Taverna","year":"2020","journal-title":"Nucleic Acids Res"},{"key":"2021090813432244500_ref129","doi-asserted-by":"crossref","first-page":"310","DOI":"10.1038\/nprot.2016.156","article-title":"A complete workflow for high-resolution spectral-stitching nanoelectrospray direct-infusion mass-spectrometry-based metabolomics and lipidomics","volume":"12","author":"Southam","year":"2016","journal-title":"Nat Protoc"},{"key":"2021090813432244500_ref130","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1038\/s41467-019-13770-6","article-title":"Heritability estimates for 361 blood metabolites across 40 genome-wide association studies","volume":"11","author":"Hagenbeek","year":"2020","journal-title":"Nat Commun"},{"key":"2021090813432244500_ref131","doi-asserted-by":"crossref","first-page":"S10","DOI":"10.1016\/j.jfma.2018.09.007","article-title":"Metabolome analysis for investigating host-gut microbiota interactions","volume":"118","author":"Chen","year":"2019","journal-title":"J Formos Med Assoc"},{"key":"2021090813432244500_ref132","doi-asserted-by":"crossref","first-page":"3619","DOI":"10.1038\/s41598-017-03249-z","article-title":"Urinary metabolomics reveals the therapeutic effect of HuangQi injections in cisplatin-induced nephrotoxic rats","volume":"7","author":"Li","year":"2017","journal-title":"Sci Rep"},{"key":"2021090813432244500_ref133","doi-asserted-by":"crossref","first-page":"1921","DOI":"10.1152\/japplphysiol.00018.2012","article-title":"Rat airway morphometry measured from in situ MRI-based geometric models","volume":"112","author":"Oakes","year":"2012","journal-title":"J Appl Physiol (1985)"},{"key":"2021090813432244500_ref134","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1016\/j.jpba.2011.04.020","article-title":"Effect of the traditional Chinese medicine tongxinluo on endothelial dysfunction rats studied by using urinary metabonomics based on liquid chromatography-mass spectrometry","volume":"56","author":"Dai","year":"2011","journal-title":"J Pharm Biomed Anal"},{"key":"2021090813432244500_ref135","doi-asserted-by":"crossref","first-page":"88697","DOI":"10.18632\/oncotarget.20733","article-title":"Pharmacometabolomics identifies dodecanamide and leukotriene B4 dimethylamide as a predictor of chemosensitivity for patients with acute myeloid leukemia treated with cytarabine and anthracycline","volume":"8","author":"Tan","year":"2017","journal-title":"Oncotarget"},{"key":"2021090813432244500_ref136","doi-asserted-by":"crossref","first-page":"e1005973","DOI":"10.1371\/journal.pcbi.1005973","article-title":"GSimp: a Gibbs sampler based left-censored missing value imputation approach for metabolomics studies","volume":"14","author":"Wei","year":"2018","journal-title":"PLoS Comput Biol"},{"key":"2021090813432244500_ref137","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1007\/978-1-4939-9236-2_20","article-title":"Pre-analytic considerations for mass spectrometry-based untargeted metabolomics data","volume":"1978","author":"Reinhold","year":"2019","journal-title":"Methods Mol Biol"},{"key":"2021090813432244500_ref138","doi-asserted-by":"crossref","first-page":"492","DOI":"10.1186\/s12859-019-3110-0","article-title":"Random forest-based imputation outperforms other methods for imputing LC\u2013MS metabolomics data: a comparative study","volume":"20","author":"Kokla","year":"2019","journal-title":"BMC Bioinformatics"},{"key":"2021090813432244500_ref139","doi-asserted-by":"crossref","first-page":"2088","DOI":"10.1093\/bioinformatics\/btg287","article-title":"A Bayesian missing value estimation method for gene expression profile data","volume":"19","author":"Oba","year":"2003","journal-title":"Bioinformatics"},{"key":"2021090813432244500_ref140","doi-asserted-by":"crossref","first-page":"571","DOI":"10.1186\/1471-2105-11-571","article-title":"Probabilistic principal component analysis for metabolomic data","volume":"11","author":"Nyamundanda","year":"2010","journal-title":"BMC Bioinformatics"},{"key":"2021090813432244500_ref141","first-page":"20","article-title":"Investigating the effects of imputation methods for modelling gene networks using a dynamic bayesian network from gene expression data","volume":"21","author":"Chai","year":"2014","journal-title":"Malays J Med Sci"},{"key":"2021090813432244500_ref142","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1007\/s11306-016-1030-9","article-title":"Non-targeted UHPLC-MS metabolomic data processing methods: a comparative investigation of normalisation, missing value imputation, transformation and scaling","volume":"12","author":"Di Guida","year":"2016","journal-title":"Metabolomics"},{"key":"2021090813432244500_ref143","doi-asserted-by":"crossref","first-page":"2955","DOI":"10.1002\/sim.6944","article-title":"Dealing with missing covariates in epidemiologic studies: a comparison between multiple imputation and a full Bayesian approach","volume":"35","author":"Erler","year":"2016","journal-title":"Stat Med"},{"key":"2021090813432244500_ref144","doi-asserted-by":"crossref","first-page":"W652","DOI":"10.1093\/nar\/gkp356","article-title":"MetaboAnalyst: a web server for metabolomic data analysis and interpretation","volume":"37","author":"Xia","year":"2009","journal-title":"Nucleic Acids Res"},{"key":"2021090813432244500_ref145","doi-asserted-by":"crossref","first-page":"359","DOI":"10.3389\/fphar.2018.00359","article-title":"Synergistic killing of polymyxin B in combination with the antineoplastic drug mitotane against Polymyxin-susceptible and -resistant Acinetobacter baumannii: a metabolomic study","volume":"9","author":"Tran","year":"2018","journal-title":"Front Pharmacol"},{"key":"2021090813432244500_ref146","doi-asserted-by":"crossref","first-page":"621","DOI":"10.1093\/bib\/bby127","article-title":"ANPELA: analysis and performance assessment of the label-free quantification workflow for metaproteomic studies","volume":"21","author":"Tang","year":"2020","journal-title":"Brief Bioinform"},{"key":"2021090813432244500_ref147","doi-asserted-by":"crossref","first-page":"520","DOI":"10.1093\/bioinformatics\/17.6.520","article-title":"Missing value estimation methods for DNA microarrays","volume":"17","author":"Troyanskaya","year":"2001","journal-title":"Bioinformatics"},{"key":"2021090813432244500_ref148","doi-asserted-by":"crossref","first-page":"9659","DOI":"10.1038\/s41598-020-66815-y","article-title":"A metabolomics approach for early prediction of vincristine-induced peripheral neuropathy","volume":"10","author":"Verma","year":"2020","journal-title":"Sci Rep"},{"key":"2021090813432244500_ref149","doi-asserted-by":"crossref","first-page":"135","DOI":"10.3389\/fphar.2016.00135","article-title":"Pharmacometabolomic assessment of metformin in non-diabetic, African Americans","volume":"7","author":"Rotroff","year":"2016","journal-title":"Front Pharmacol"},{"key":"2021090813432244500_ref150","doi-asserted-by":"crossref","first-page":"669","DOI":"10.1002\/psp4.12017","article-title":"Pharmacometabolomic assessments of atenolol and hydrochlorothiazide treatment reveal novel drug response phenotypes","volume":"4","author":"Rotroff","year":"2015","journal-title":"CPT Pharmacometrics Syst Pharmacol"},{"key":"2021090813432244500_ref151","doi-asserted-by":"crossref","first-page":"10101","DOI":"10.1073\/pnas.97.18.10101","article-title":"Singular value decomposition for genome-wide expression data processing and modeling","volume":"97","author":"Alter","year":"2000","journal-title":"Proc Natl Acad Sci USA"},{"key":"2021090813432244500_ref152","doi-asserted-by":"crossref","first-page":"1608","DOI":"10.1093\/nar\/gkl047","article-title":"Microarray missing data imputation based on a set theoretic framework and biological knowledge","volume":"34","author":"Gan","year":"2006","journal-title":"Nucleic Acids Res"},{"key":"2021090813432244500_ref153","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1007\/s11306-018-1431-z","article-title":"Using urine metabolomics to understand the pathogenesis of infant respiratory syncytial virus (RSV) infection and its role in childhood wheezing","volume":"14","author":"Turi","year":"2018","journal-title":"Metabolomics"},{"key":"2021090813432244500_ref154","doi-asserted-by":"crossref","first-page":"2437608","DOI":"10.1155\/2017\/2437608","article-title":"Metabolomic biomarker identification in presence of outliers and missing values","volume":"2017","author":"Kumar","year":"2017","journal-title":"Biomed Res Int"},{"key":"2021090813432244500_ref155","doi-asserted-by":"crossref","first-page":"1374","DOI":"10.1161\/CIRCULATIONAHA.117.031139","article-title":"Functional metabolomics characterizes a key role for N-acetylneuraminic acid in coronary artery diseases","volume":"137","author":"Zhang","year":"2018","journal-title":"Circulation"},{"key":"2021090813432244500_ref156","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1007\/978-1-4939-7643-0_2","article-title":"Quality control and validation issues in LC\u2013MS metabolomics","volume":"1738","author":"Begou","year":"2018","journal-title":"Methods Mol Biol"},{"key":"2021090813432244500_ref157","doi-asserted-by":"crossref","first-page":"2741","DOI":"10.1038\/s41598-019-39235-w","article-title":"Untargeted metabolomics by high resolution mass spectrometry coupled to normal and reversed phase liquid chromatography as a tool to study the in vitro biotransformation of new psychoactive substances","volume":"9","author":"Manier","year":"2019","journal-title":"Sci Rep"},{"key":"2021090813432244500_ref158","doi-asserted-by":"crossref","first-page":"9836","DOI":"10.1021\/acs.analchem.9b01505","article-title":"Guidelines for selection of internal standard-based normalization strategies in untargeted lipidomic profiling by LC-HR-MS\/MS","volume":"91","author":"Drotleff","year":"2019","journal-title":"Anal Chem"},{"key":"2021090813432244500_ref159","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1007\/s11306-016-1026-5","article-title":"Normalization and integration of large-scale metabolomics data using support vector regression","volume":"12","author":"Shen","year":"2016","journal-title":"Metabolomics"},{"key":"2021090813432244500_ref160","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1016\/j.aca.2018.08.002","article-title":"statTarget: a streamlined tool for signal drift correction and interpretations of quantitative mass spectrometry-based omics data","volume":"1036","author":"Luan","year":"2018","journal-title":"Anal Chim Acta"},{"key":"2021090813432244500_ref161","doi-asserted-by":"crossref","first-page":"1379","DOI":"10.1109\/TPAMI.2005.183","article-title":"Principal surfaces from unsupervised kernel regression","volume":"27","author":"Meinicke","year":"2005","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"2021090813432244500_ref162","doi-asserted-by":"crossref","first-page":"2086","DOI":"10.1109\/TNNLS.2014.2305193","article-title":"Local linear regression for function learning: an analysis based on sample discrepancy","volume":"25","author":"Cervellera","year":"2014","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"2021090813432244500_ref163","doi-asserted-by":"crossref","first-page":"164","DOI":"10.1177\/0748730403261630","article-title":"Local polynomial regression modeling of human plasma melatonin levels","volume":"19","author":"Gamst","year":"2004","journal-title":"J Biol Rhythms"},{"key":"2021090813432244500_ref164","doi-asserted-by":"crossref","first-page":"447","DOI":"10.1186\/s13071-019-3703-5","article-title":"Serum metabolomic alterations in beagle dogs experimentally infected with Toxocara canis","volume":"12","author":"Zheng","year":"2019","journal-title":"Parasit Vectors"},{"key":"2021090813432244500_ref165","doi-asserted-by":"crossref","first-page":"1817","DOI":"10.1093\/molbev\/msz107","article-title":"Robust method for detecting convergent shifts in evolutionary rates","volume":"36","author":"Partha","year":"2019","journal-title":"Mol Biol Evol"},{"issue":"4","key":"2021090813432244500_ref166","doi-asserted-by":"crossref","first-page":"e19","DOI":"10.1136\/bjsm.2004.013078","article-title":"The repeatability and criterion related validity of the 20 m multistage fitness test as a predictor of maximal oxygen uptake in active young men","volume":"39","author":"Cooper","year":"2005","journal-title":"Br J Sports Med"},{"issue":"2","key":"2021090813432244500_ref167","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1093\/eurheartj\/ehl449","article-title":"Long-term clinical variation of NT-proBNP in stable chronic heart failure patients","volume":"28","author":"Schou","year":"2007","journal-title":"Eur Heart J"},{"issue":"7066","key":"2021090813432244500_ref168","doi-asserted-by":"crossref","first-page":"1200","DOI":"10.1136\/bmj.313.7066.1200","article-title":"Detecting skewness from summary information","volume":"313","author":"Altman","year":"1996","journal-title":"BMJ"},{"issue":"24","key":"2021090813432244500_ref169","doi-asserted-by":"crossref","first-page":"10768","DOI":"10.1021\/ac302748b","article-title":"Normalizing and integrating metabolomics data","volume":"84","author":"De Livera","year":"2012","journal-title":"Anal Chem"},{"key":"2021090813432244500_ref170","doi-asserted-by":"crossref","first-page":"1683","DOI":"10.1074\/mcp.RA118.001169","article-title":"Simultaneous improvement in the precision, accuracy, and robustness of label-free proteome quantification by optimizing data manipulation chains","volume":"18","author":"Tang","year":"2019","journal-title":"Mol Cell Proteomics"},{"issue":"2","key":"2021090813432244500_ref171","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1111\/j.2517-6161.1964.tb00553.x","article-title":"An analysis of transformations","volume":"26","author":"Box","year":"1964","journal-title":"J R Stat Soc Series B Stat Methodol"},{"issue":"2","key":"2021090813432244500_ref172","doi-asserted-by":"crossref","first-page":"126","DOI":"10.4103\/0976-500X.72373","article-title":"Data transformation","volume":"1","author":"Manikandan","year":"2010","journal-title":"J Pharmacol Pharmacother"},{"key":"2021090813432244500_ref173","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1186\/1471-2164-7-142","article-title":"Centering, scaling, and transformations: improving the biological information content of metabolomics data","volume":"7","author":"Berg","year":"2006","journal-title":"BMC Genomics"},{"issue":"11","key":"2021090813432244500_ref174","doi-asserted-by":"crossref","first-page":"560","DOI":"10.1089\/omi.2019.0140","article-title":"Time-dependent changes in urinary metabolome before and after intensive phase tuberculosis therapy: a pharmacometabolomics study","volume":"23","author":"Combrink","year":"2019","journal-title":"OMICS"},{"issue":"7","key":"2021090813432244500_ref175","doi-asserted-by":"crossref","first-page":"2501","DOI":"10.1002\/chem.201503496","article-title":"One-pot sequential propargylation\/cycloisomerization: a facile [4+2]-benzannulation approach to carbazoles","volume":"22","author":"Raji Reddy","year":"2016","journal-title":"Chemistry"},{"key":"2021090813432244500_ref176","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1016\/j.aca.2017.09.019","article-title":"Optimal preprocessing of serum and urine metabolomic data fusion for staging prostate cancer through design of experiment","volume":"991","author":"Zheng","year":"2017","journal-title":"Anal Chim Acta"},{"issue":"3","key":"2021090813432244500_ref177","doi-asserted-by":"crossref","first-page":"657","DOI":"10.1007\/s10549-018-4862-3","article-title":"Pharmacometabolomics reveals a role for histidine, phenylalanine, and threonine in the development of paclitaxel-induced peripheral neuropathy","volume":"171","author":"Sun","year":"2018","journal-title":"Breast Cancer Res Treat"},{"issue":"2","key":"2021090813432244500_ref178","first-page":"169","article-title":"The Box-Cox transformation technique\u2014a review","volume":"41","author":"Sakia","year":"1992","journal-title":"J R Stat Soc Series D Stat"},{"issue":"2","key":"2021090813432244500_ref179","doi-asserted-by":"crossref","first-page":"547","DOI":"10.1002\/hep.30319","article-title":"Serum metabolites as diagnostic biomarkers for cholangiocarcinoma, hepatocellular carcinoma, and primary sclerosing cholangitis","volume":"70","author":"Banales","year":"2019","journal-title":"Hepatology"},{"issue":"11","key":"2021090813432244500_ref180","doi-asserted-by":"crossref","first-page":"140","DOI":"10.1007\/s11306-017-1274-z","article-title":"A metabolomics-based approach for non-invasive diagnosis of chromosomal anomalies","volume":"13","author":"Troisi","year":"2017","journal-title":"Metabolomics"},{"issue":"1","key":"2021090813432244500_ref181","doi-asserted-by":"crossref","first-page":"96","DOI":"10.2174\/157489312799304431","article-title":"Bioinformatics tools for mass spectroscopy-based metabolomic data processing and analysis","volume":"7","author":"Sugimoto","year":"2012","journal-title":"Curr Bioinform"},{"key":"2021090813432244500_ref182","doi-asserted-by":"crossref","first-page":"2142","DOI":"10.1093\/bib\/bbz137","article-title":"A novel bioinformatics approach to identify the consistently well-performing normalization strategy for current metabolomic studies","volume":"21","author":"Yang","year":"2020","journal-title":"Brief Bioinform"},{"issue":"1","key":"2021090813432244500_ref183","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1089\/106652703763255697","article-title":"Contrast normalization of oligonucleotide arrays","volume":"10","author":"Astrand","year":"2003","journal-title":"J Comput Biol"},{"key":"2021090813432244500_ref184","doi-asserted-by":"crossref","first-page":"681","DOI":"10.3389\/fphar.2018.00681","article-title":"Discovery of the consistently well-performed analysis chain for SWATH-MS based pharmacoproteomic quantification","volume":"9","author":"Fu","year":"2018","journal-title":"Front Pharmacol"},{"issue":"2","key":"2021090813432244500_ref185","doi-asserted-by":"crossref","first-page":"619","DOI":"10.1021\/acs.jproteome.6b00704","article-title":"Correlation patterns in experimental data are affected by normalization procedures: consequences for data analysis and network inference","volume":"16","author":"Saccenti","year":"2017","journal-title":"J Proteome Res"},{"issue":"9","key":"2021090813432244500_ref186","doi-asserted-by":"crossref","first-page":"473","DOI":"10.1089\/omi.2013.0010","article-title":"Evaluation of normalization methods to pave the way towards large-scale LC\u2013MS-based metabolomics profiling experiments","volume":"17","author":"Ejigu","year":"2013","journal-title":"OMICS"},{"issue":"12","key":"2021090813432244500_ref187","doi-asserted-by":"crossref","first-page":"e116221","DOI":"10.1371\/journal.pone.0116221","article-title":"Metabolomics data normalization with EigenMS","volume":"9","author":"Karpievitch","year":"2014","journal-title":"PLoS One"},{"issue":"19","key":"2021090813432244500_ref188","doi-asserted-by":"crossref","first-page":"2573","DOI":"10.1093\/bioinformatics\/btp426","article-title":"Normalization of peak intensities in bottom-up MS-based proteomics using singular value decomposition","volume":"25","author":"Karpievitch","year":"2009","journal-title":"Bioinformatics"},{"issue":"9","key":"2021090813432244500_ref189","doi-asserted-by":"crossref","first-page":"1724","DOI":"10.1371\/journal.pgen.0030161","article-title":"Capturing heterogeneity in gene expression studies by surrogate variable analysis","volume":"3","author":"Leek","year":"2007","journal-title":"PLoS Genet"},{"issue":"1","key":"2021090813432244500_ref190","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1007\/s00299-005-0037-x","article-title":"Changes in gene expression in maize kernel in response to water and salt stress","volume":"25","author":"Andjelkovic","year":"2006","journal-title":"Plant Cell Rep"},{"issue":"18","key":"2021090813432244500_ref191","doi-asserted-by":"crossref","first-page":"4818","DOI":"10.1021\/ac026468x","article-title":"Quantification of proteins and metabolites by mass spectrometry without isotopic labeling or spiked standards","volume":"75","author":"Wang","year":"2003","journal-title":"Anal Chem"},{"issue":"10","key":"2021090813432244500_ref192","doi-asserted-by":"crossref","first-page":"1464","DOI":"10.1021\/ac60266a027","article-title":"Computer methods in analytical mass spectrometry\u2014identification of an unknown compound in a catalog","volume":"40","author":"Crawford","year":"1968","journal-title":"Anal Chem"},{"issue":"5\u20136","key":"2021090813432244500_ref193","doi-asserted-by":"crossref","first-page":"547","DOI":"10.1016\/j.jchromb.2009.01.007","article-title":"Normalization strategies for metabonomic analysis of urine samples","volume":"877","author":"Warrack","year":"2009","journal-title":"J Chromatogr B Analyt Technol Biomed Life Sci"},{"issue":"3","key":"2021090813432244500_ref194","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1007\/s11306-018-1321-4","article-title":"Recommended strategies for spectral processing and post-processing of 1D (1)H-NMR data of biofluids with a particular focus on urine","volume":"14","author":"Emwas","year":"2018","journal-title":"Metabolomics"},{"issue":"2","key":"2021090813432244500_ref195","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1093\/bioinformatics\/19.2.185","article-title":"A comparison of normalization methods for high density oligonucleotide array data based on variance and bias","volume":"19","author":"Bolstad","year":"2003","journal-title":"Bioinformatics"},{"issue":"7","key":"2021090813432244500_ref196","doi-asserted-by":"crossref","first-page":"3606","DOI":"10.1021\/ac502439y","article-title":"Statistical methods for handling unwanted variation in metabolomics data","volume":"87","author":"De Livera","year":"2015","journal-title":"Anal Chem"},{"key":"2021090813432244500_ref197","doi-asserted-by":"crossref","first-page":"38881","DOI":"10.1038\/srep38881","article-title":"Performance evaluation and online realization of data-driven normalization methods used in LC\/MS based untargeted metabolomics analysis","volume":"6","author":"Li","year":"2016","journal-title":"Sci Rep"},{"issue":"3","key":"2021090813432244500_ref198","doi-asserted-by":"crossref","first-page":"541","DOI":"10.1093\/bib\/bbv077","article-title":"Bioinformatics tools for the analysis of NMR metabolomics studies focused on the identification of clinically relevant biomarkers","volume":"17","author":"Puchades-Carrasco","year":"2016","journal-title":"Brief Bioinform"},{"issue":"12 Pt A","key":"2021090813432244500_ref199","doi-asserted-by":"crossref","first-page":"2395","DOI":"10.1016\/j.bbadis.2014.09.014","article-title":"Region-specific metabolic alterations in the brain of the APP\/PS1 transgenic mice of Alzheimer's disease","volume":"1842","author":"Gonz\u00e1lez-Dom\u00ednguez","year":"2014","journal-title":"Biochim Biophys Acta"},{"issue":"11","key":"2021090813432244500_ref200","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1007\/s11306-018-1445-6","article-title":"Metabolomic biomarkers and novel dietary factors associated with gestational diabetes in China","volume":"14","author":"Chen","year":"2018","journal-title":"Metabolomics"},{"issue":"4","key":"2021090813432244500_ref201","doi-asserted-by":"crossref","first-page":"e59909","DOI":"10.1371\/journal.pone.0059909","article-title":"Human breath analysis may support the existence of individual metabolic phenotypes","volume":"8","author":"Martinez-Lozano Sinues","year":"2013","journal-title":"PLoS One"},{"key":"2021090813432244500_ref202","doi-asserted-by":"crossref","first-page":"127","DOI":"10.3389\/fphar.2019.00127","article-title":"Assessing the effectiveness of direct data merging strategy in long-term and large-scale pharmacometabonomics","volume":"10","author":"Cui","year":"2019","journal-title":"Front Pharmacol"},{"issue":"13","key":"2021090813432244500_ref203","doi-asserted-by":"crossref","first-page":"4281","DOI":"10.1021\/ac051632c","article-title":"Probabilistic quotient normalization as robust method to account for dilution of complex biological mixtures. Application in 1H NMR metabonomics","volume":"78","author":"Dieterle","year":"2006","journal-title":"Anal Chem"},{"issue":"12","key":"2021090813432244500_ref204","doi-asserted-by":"crossref","first-page":"6334","DOI":"10.1021\/acs.analchem.6b00603","article-title":"SMART: statistical metabolomics analysis\u2014an R tool","volume":"88","author":"Liang","year":"2016","journal-title":"Anal Chem"},{"issue":"3","key":"2021090813432244500_ref205","doi-asserted-by":"crossref","first-page":"1248","DOI":"10.1021\/acs.jproteome.7b00859","article-title":"Pharmacometabonomics analysis reveals serum formate and acetate potentially associated with varying response to gemcitabine-carboplatin chemotherapy in metastatic breast cancer patients","volume":"17","author":"Jiang","year":"2018","journal-title":"J Proteome Res"},{"key":"2021090813432244500_ref206","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1016\/j.trac.2013.09.005","article-title":"Mass-spectrometry-based metabolomics analysis for foodomics","volume":"52","author":"Hu","year":"2013","journal-title":"Trends Anal Chem"},{"issue":"6","key":"2021090813432244500_ref207","doi-asserted-by":"crossref","first-page":"1684","DOI":"10.1074\/mcp.M114.046508","article-title":"Optimized analytical procedures for the untargeted metabolomic profiling of human urine and plasma by combining hydrophilic interaction (HILIC) and reverse-phase liquid chromatography (RPLC)-mass spectrometry","volume":"14","author":"Contrepois","year":"2015","journal-title":"Mol Cell Proteomics"},{"issue":"3","key":"2021090813432244500_ref208","doi-asserted-by":"crossref","first-page":"419","DOI":"10.1007\/s00216-004-2783-y","article-title":"Using chemometrics for navigating in the large data sets of genomics, proteomics, and metabonomics (gpm)","volume":"380","author":"Eriksson","year":"2004","journal-title":"Anal Bioanal Chem"},{"issue":"20","key":"2021090813432244500_ref209","doi-asserted-by":"crossref","first-page":"6729","DOI":"10.1021\/ac051080y","article-title":"Fusion of mass spectrometry-based metabolomics data","volume":"77","author":"Smilde","year":"2005","journal-title":"Anal Chem"},{"issue":"1\u20132","key":"2021090813432244500_ref210","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1016\/S0003-2670(03)00094-1","article-title":"Improved analysis of multivariate data by variable stability scaling: application to NMR-based metabolic profiling","volume":"490","author":"Keun","year":"2003","journal-title":"Anal Chim Acta"},{"key":"2021090813432244500_ref211","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1016\/j.chroma.2013.01.111","article-title":"Liquid chromatography tandem mass spectrometry study of urinary nucleosides as potential cancer markers","volume":"1283","author":"Struck","year":"2013","journal-title":"J Chromatogr A"},{"issue":"3","key":"2021090813432244500_ref212","doi-asserted-by":"crossref","first-page":"1116","DOI":"10.1021\/ac103011b","article-title":"Technical and biological variation in UPLC-MS-based untargeted metabolic profiling of liver extracts: application in an experimental toxicity study on galactosamine","volume":"83","author":"Masson","year":"2011","journal-title":"Anal Chem"},{"issue":"4","key":"2021090813432244500_ref213","doi-asserted-by":"crossref","first-page":"643","DOI":"10.1007\/s11306-011-0357-5","article-title":"Serum amino acid profiles and their alterations in colorectal cancer","volume":"8","author":"Leichtle","year":"2012","journal-title":"Metabolomics"},{"issue":"3","key":"2021090813432244500_ref214","doi-asserted-by":"crossref","first-page":"684","DOI":"10.1007\/s11306-014-0738-7","article-title":"The influence of scaling metabolomics data on model classification accuracy","volume":"11","author":"Gromski","year":"2015","journal-title":"Metabolomics"},{"issue":"10","key":"2021090813432244500_ref215","doi-asserted-by":"crossref","first-page":"2285","DOI":"10.1074\/mcp.M800514-MCP200","article-title":"Development and evaluation of normalization methods for label-free relative quantification of endogenous peptides","volume":"8","author":"Kultima","year":"2009","journal-title":"Mol Cell Proteomics"},{"issue":"Suppl 1","key":"2021090813432244500_ref216","doi-asserted-by":"crossref","first-page":"S96","DOI":"10.1093\/bioinformatics\/18.suppl_1.S96","article-title":"Variance stabilization applied to microarray data calibration and to the quantification of differential expression","volume":"18","author":"Huber","year":"2002","journal-title":"Bioinformatics"},{"issue":"8","key":"2021090813432244500_ref217","doi-asserted-by":"crossref","first-page":"3217","DOI":"10.1021\/acs.jproteome.5b00192","article-title":"Data normalization of (1)H NMR metabolite fingerprinting data sets in the presence of unbalanced metabolite regulation","volume":"14","author":"Hochrein","year":"2015","journal-title":"J Proteome Res"},{"key":"2021090813432244500_ref218","doi-asserted-by":"crossref","first-page":"310372","DOI":"10.1155\/2014\/310372","article-title":"Metabolomic analysis of liver tissue from the VX2 rabbit model of secondary liver tumors","volume":"2014","author":"Ibarra","year":"2014","journal-title":"HPB Surg"},{"issue":"15","key":"2021090813432244500_ref219","doi-asserted-by":"crossref","first-page":"6080","DOI":"10.1021\/ac900424c","article-title":"Interdependence of signal processing and analysis of urine 1H NMR spectra for metabolic profiling","volume":"81","author":"Zhang","year":"2009","journal-title":"Anal Chem"},{"key":"2021090813432244500_ref220","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/j.mex.2017.02.004","article-title":"Metabolic profiling of body fluids and multivariate data analysis","volume":"4","author":"Trezzi","year":"2017","journal-title":"MethodsX"},{"issue":"19","key":"2021090813432244500_ref221","doi-asserted-by":"crossref","first-page":"7974","DOI":"10.1021\/ac901143w","article-title":"Compensation for systematic cross-contribution improves normalization of mass spectrometry based metabolomics data","volume":"81","author":"Redestig","year":"2009","journal-title":"Anal Chem"},{"issue":"1","key":"2021090813432244500_ref222","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1093\/biostatistics\/kxv026","article-title":"Correcting gene expression data when neither the unwanted variation nor the factor of interest are observed","volume":"17","author":"Jacob","year":"2016","journal-title":"Biostatistics"},{"issue":"3","key":"2021090813432244500_ref223","doi-asserted-by":"crossref","first-page":"539","DOI":"10.1093\/biostatistics\/kxr034","article-title":"Using control genes to correct for unwanted variation in microarray data","volume":"13","author":"Gagnon-Bartsch","year":"2012","journal-title":"Biostatistics"},{"key":"2021090813432244500_ref224","doi-asserted-by":"crossref","first-page":"474","DOI":"10.3389\/fphar.2017.00474","article-title":"Metabolomics and integrative omics for the development of Thai traditional medicine","volume":"8","author":"Khoomrung","year":"2017","journal-title":"Front Pharmacol"},{"issue":"18","key":"2021090813432244500_ref225","doi-asserted-by":"crossref","first-page":"11124","DOI":"10.1021\/acs.analchem.8b03065","article-title":"MetaboGroup S: a group entropy-based web platform for evaluating normalization methods in blood metabolomics data from maintenance hemodialysis patients","volume":"90","author":"Wang","year":"2018","journal-title":"Anal Chem"},{"issue":"2","key":"2021090813432244500_ref226","doi-asserted-by":"crossref","first-page":"789","DOI":"10.1214\/20-AOAS1328","article-title":"Inference in metabolomics with nonrandom missing data and latent factors","volume":"14","author":"McKennan","year":"2020","journal-title":"Ann Appl Stat"},{"issue":"4","key":"2021090813432244500_ref227","doi-asserted-by":"crossref","first-page":"274","DOI":"10.1016\/j.tibs.2017.01.004","article-title":"Metabolomics: a primer","volume":"42","author":"Liu","year":"2017","journal-title":"Trends Biochem Sci"},{"key":"2021090813432244500_ref228","doi-asserted-by":"crossref","first-page":"198363","DOI":"10.1155\/2015\/198363","article-title":"Review of feature selection and feature extraction methods applied on microarray data","volume":"2015","author":"Hira","year":"2015","journal-title":"Adv Bioinformatics"},{"issue":"3","key":"2021090813432244500_ref229","doi-asserted-by":"crossref","first-page":"699","DOI":"10.1213\/ANE.0b013e31827f53d7","article-title":"Statistical grand rounds: a review of analysis and sample size calculation considerations for Wilcoxon tests","volume":"117","author":"Divine","year":"2013","journal-title":"Anesth Analg"},{"issue":"1","key":"2021090813432244500_ref230","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1111\/j.1541-0420.2005.00389.x","article-title":"The Wilcoxon signed rank test for paired comparisons of clustered data","volume":"62","author":"Rosner","year":"2006","journal-title":"Biometrics"},{"issue":"1","key":"2021090813432244500_ref231","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1177\/1099800418789777","article-title":"A large randomized trial: effects of mindfulness-based stress reduction (MBSR) for breast cancer (BC) survivors on salivary cortisol and IL-6","volume":"21","author":"Lengacher","year":"2019","journal-title":"Biol Res Nurs"},{"issue":"2","key":"2021090813432244500_ref232","doi-asserted-by":"crossref","first-page":"143","DOI":"10.11613\/BM.2013.018","article-title":"The chi-square test of independence","volume":"23","author":"McHugh","year":"2013","journal-title":"Biochem Med"},{"issue":"10","key":"2021090813432244500_ref233","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1007\/s11306-019-1601-7","article-title":"Predicting response to lisinopril in treating hypertension: a pilot study","volume":"15","author":"Sonn","year":"2019","journal-title":"Metabolomics"},{"issue":"3","key":"2021090813432244500_ref234","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1109\/MCI.2018.2840660","article-title":"Augmentation of physician assessments with multi-omics enhances predictability of drug response: a case study of major depressive disorder","volume":"13","author":"Athreya","year":"2018","journal-title":"IEEE Comput Intell Mag"},{"key":"2021090813432244500_ref235","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/j.jchromb.2018.07.016","article-title":"SLive_RefAppend (1)H NMR based pharmacometabolomics analysis of metabolic phenotype on predicting metabolism characteristics of losartan in healthy volunteers","volume":"1095","author":"He","year":"2018","journal-title":"J Chromatogr B Analyt Technol Biomed Life Sci"},{"issue":"26","key":"2021090813432244500_ref236","doi-asserted-by":"crossref","first-page":"39809","DOI":"10.18632\/oncotarget.9489","article-title":"Pharmacometabolomics study identifies circulating spermidine and tryptophan as potential biomarkers associated with the complete pathological response to trastuzumab-paclitaxel neoadjuvant therapy in HER-2 positive breast cancer","volume":"7","author":"Miolo","year":"2016","journal-title":"Oncotarget"},{"issue":"10","key":"2021090813432244500_ref237","doi-asserted-by":"crossref","first-page":"553","DOI":"10.1038\/nrneurol.2009.139","article-title":"Predicting responders to therapies for multiple sclerosis","volume":"5","author":"R\u00edo","year":"2009","journal-title":"Nat Rev Neurol"},{"issue":"1","key":"2021090813432244500_ref238","doi-asserted-by":"crossref","first-page":"1","DOI":"10.3233\/JAD-181121","article-title":"The Alzheimer precision medicine initiative","volume":"68","author":"Hampel","year":"2019","journal-title":"J Alzheimers Dis"},{"key":"2021090813432244500_ref239","doi-asserted-by":"crossref","first-page":"1527","DOI":"10.3389\/fimmu.2020.01527","article-title":"Using serum metabolomics to predict development of anti-drug antibodies in multiple sclerosis patients treated with IFN\u03b2","volume":"11","author":"Waddington","year":"2020","journal-title":"Front Immunol"},{"issue":"6","key":"2021090813432244500_ref240","doi-asserted-by":"crossref","first-page":"591","DOI":"10.1111\/jvp.12884","article-title":"Pharmacometabolomics with a combination of PLS-DA and random forest algorithm analyses reveal meloxicam alters feline plasma metabolite profiles","volume":"43","author":"Broughton-Neiswanger","year":"2020","journal-title":"J Vet Pharmacol Ther"},{"issue":"2","key":"2021090813432244500_ref241","doi-asserted-by":"crossref","first-page":"390","DOI":"10.1093\/toxsci\/kfw001","article-title":"A systematic strategy for screening and application of specific biomarkers in hepatotoxicity using metabolomics combined with ROC curves and SVMs","volume":"150","author":"Li","year":"2016","journal-title":"Toxicol Sci"},{"key":"2021090813432244500_ref242","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1186\/1471-2105-9-179","article-title":"Multidimensional scaling for large genomic data sets","volume":"9","author":"Tzeng","year":"2008","journal-title":"BMC Bioinformatics"},{"key":"2021090813432244500_ref243","doi-asserted-by":"crossref","first-page":"628","DOI":"10.1186\/1471-2407-10-628","article-title":"Merging transcriptomics and metabolomics\u2014advances in breast cancer profiling","volume":"10","author":"Borgan","year":"2010","journal-title":"BMC Cancer"},{"issue":"5500","key":"2021090813432244500_ref244","doi-asserted-by":"crossref","first-page":"2319","DOI":"10.1126\/science.290.5500.2319","article-title":"A global geometric framework for nonlinear dimensionality reduction","volume":"290","author":"Tenenbaum","year":"2000","journal-title":"Science"},{"key":"2021090813432244500_ref245","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1186\/s13020-020-00330-0","article-title":"Transcriptomics- and metabolomics-based integration analyses revealed the potential pharmacological effects and functional pattern of in vivo radix Paeoniae Alba administration","volume":"15","author":"Wang","year":"2020","journal-title":"Chinas Med"},{"issue":"5500","key":"2021090813432244500_ref246","doi-asserted-by":"crossref","first-page":"2323","DOI":"10.1126\/science.290.5500.2323","article-title":"Nonlinear dimensionality reduction by locally linear embedding","volume":"290","author":"Roweis","year":"2000","journal-title":"Science"},{"key":"2021090813432244500_ref247","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1007\/978-1-0716-0301-7_8","article-title":"Visualization of single cell RNA-Seq data using t-SNE in R","volume":"2117","author":"Zhou","year":"2020","journal-title":"Methods Mol Biol"},{"issue":"12","key":"2021090813432244500_ref248","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1007\/s11306-016-1131-5","article-title":"Metabolomics profiling of concussion in adolescent male hockey players: a novel diagnostic method","volume":"12","author":"Daley","year":"2016","journal-title":"Metabolomics"},{"key":"2021090813432244500_ref249","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1007\/978-3-319-47656-8_1","article-title":"Metabolomics: definitions and significance in systems biology","volume":"965","author":"Klassen","year":"2017","journal-title":"Adv Exp Med Biol"},{"issue":"9\u201310","key":"2021090813432244500_ref250","doi-asserted-by":"crossref","first-page":"1746","DOI":"10.1002\/jssc.202000060","article-title":"Current status of retention time prediction in metabolite identification","volume":"43","author":"Witting","year":"2020","journal-title":"J Sep Sci"},{"issue":"10","key":"2021090813432244500_ref251","doi-asserted-by":"crossref","first-page":"389","DOI":"10.3390\/metabo10100389","article-title":"Circulating metabolites as potential biomarkers for neurological disorders-metabolites in neurological disorders","volume":"10","author":"Donatti","year":"2020","journal-title":"Metabolites"},{"issue":"41","key":"2021090813432244500_ref252","doi-asserted-by":"crossref","first-page":"12549","DOI":"10.1073\/pnas.1516878112","article-title":"Illuminating the dark matter in metabolomics","volume":"112","author":"Silva","year":"2015","journal-title":"Proc Natl Acad Sci USA"},{"issue":"Database issue","key":"2021090813432244500_ref253","first-page":"D801","article-title":"HMDB 3.0\u2013the human metabolome database in 2013","volume":"41","author":"Wishart","year":"2013","journal-title":"Nucleic Acids Res"},{"issue":"1","key":"2021090813432244500_ref254","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1002\/cpt.134","article-title":"Metabolomic signatures for drug response phenotypes: pharmacometabolomics enables precision medicine","volume":"98","author":"Kaddurah-Daouk","year":"2015","journal-title":"Clin Pharmacol Ther"},{"issue":"D1","key":"2021090813432244500_ref255","doi-asserted-by":"crossref","first-page":"D1102","DOI":"10.1093\/nar\/gky1033","article-title":"PubChem 2019 update: improved access to chemical data","volume":"47","author":"Kim","year":"2019","journal-title":"Nucleic Acids Res"},{"issue":"D1","key":"2021090813432244500_ref256","doi-asserted-by":"crossref","first-page":"D1202","DOI":"10.1093\/nar\/gkv951","article-title":"PubChem substance and compound databases","volume":"44","author":"Kim","year":"2016","journal-title":"Nucleic Acids Res"},{"issue":"D1","key":"2021090813432244500_ref257","doi-asserted-by":"crossref","first-page":"D955","DOI":"10.1093\/nar\/gkw1118","article-title":"PubChem BioAssay: 2017 update","volume":"45","author":"Wang","year":"2017","journal-title":"Nucleic Acids Res"},{"issue":"21","key":"2021090813432244500_ref258","doi-asserted-by":"crossref","first-page":"12752","DOI":"10.1021\/acs.analchem.8b03118","article-title":"Evaluation of an artificial neural network retention index model for chemical structure identification in nontargeted metabolomics","volume":"90","author":"Samaraweera","year":"2018","journal-title":"Anal Chem"},{"issue":"7","key":"2021090813432244500_ref259","doi-asserted-by":"crossref","first-page":"703","DOI":"10.1002\/jms.1777","article-title":"MassBank: a public repository for sharing mass spectral data for life sciences","volume":"45","author":"Horai","year":"2010","journal-title":"J Mass Spectrom"},{"key":"2021090813432244500_ref260","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1016\/j.phytochem.2013.02.018","article-title":"Accurate mass-time tag library for LC\/MS-based metabolite profiling of medicinal plants","volume":"91","author":"Cuthbertson","year":"2013","journal-title":"Phytochemistry"},{"issue":"2","key":"2021090813432244500_ref261","doi-asserted-by":"crossref","first-page":"31","DOI":"10.3390\/metabo8020031","article-title":"Software tools and approaches for compound identification of LC\u2013MS\/MS data in metabolomics","volume":"8","author":"Bla\u017eenovi\u0107","year":"2018","journal-title":"Metabolites"},{"issue":"5","key":"2021090813432244500_ref262","doi-asserted-by":"crossref","first-page":"3156","DOI":"10.1021\/acs.analchem.7b04424","article-title":"METLIN: a technology platform for identifying knowns and unknowns","volume":"90","author":"Guijas","year":"2018","journal-title":"Anal Chem"},{"issue":"8","key":"2021090813432244500_ref263","doi-asserted-by":"crossref","first-page":"306","DOI":"10.3390\/metabo10080306","article-title":"Comparative untargeted metabolomics analysis of the psychostimulants 3,4-methylenedioxy-methamphetamine (MDMA), amphetamine, and the novel psychoactive substance mephedrone after controlled drug administration to humans","volume":"10","author":"Steuer","year":"2020","journal-title":"Metabolites"},{"issue":"2","key":"2021090813432244500_ref264","doi-asserted-by":"crossref","first-page":"291","DOI":"10.1021\/ed200088u","article-title":"LIPID MAPS-nature lipidomics gateway: an online resource for students and educators interested in lipids","volume":"89","author":"Sud","year":"2012","journal-title":"J Chem Educ"},{"issue":"4","key":"2021090813432244500_ref265","doi-asserted-by":"crossref","first-page":"282","DOI":"10.2174\/1574893614666190304125221","article-title":"Morphological segmentation analysis and texture-based support vector machines classification on mice liver fibrosis microscopic images","volume":"14","author":"Wang","year":"2019","journal-title":"Curr Bioinform"},{"issue":"4","key":"2021090813432244500_ref266","doi-asserted-by":"crossref","first-page":"685","DOI":"10.1093\/bioinformatics\/bty679","article-title":"LipidFinder on LIPID MAPS: peak filtering, MS searching and statistical analysis for lipidomics","volume":"35","author":"Fahy","year":"2019","journal-title":"Bioinformatics"},{"issue":"Database issue","key":"2021090813432244500_ref267","first-page":"D344","article-title":"ChEBI: a database and ontology for chemical entities of biological interest","volume":"36","author":"Degtyarenko","year":"2008","journal-title":"Nucleic Acids Res"},{"issue":"D1","key":"2021090813432244500_ref268","doi-asserted-by":"crossref","first-page":"D1214","DOI":"10.1093\/nar\/gkv1031","article-title":"ChEBI in 2016: improved services and an expanding collection of metabolites","volume":"44","author":"Hastings","year":"2016","journal-title":"Nucleic Acids Res"},{"issue":"1","key":"2021090813432244500_ref269","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1186\/s12859-015-0486-3","article-title":"BiNChE: a web tool and library for chemical enrichment analysis based on the ChEBI ontology","volume":"16","author":"Moreno","year":"2015","journal-title":"BMC Bioinformatics"},{"key":"2021090813432244500_ref270","first-page":"D1031","article-title":"Therapeutic target database 2020: enriched resource for facilitating research and early development of targeted therapeutics","volume":"48","author":"Wang","year":"2020","journal-title":"Nucleic Acids Res"},{"key":"2021090813432244500_ref271","doi-asserted-by":"crossref","first-page":"D1121","DOI":"10.1093\/nar\/gkx1076","article-title":"Therapeutic target database update 2018: enriched resource for facilitating bench-to-clinic research of targeted therapeutics","volume":"46","author":"Li","year":"2018","journal-title":"Nucleic Acids Res"},{"key":"2021090813432244500_ref272","doi-asserted-by":"crossref","first-page":"120710","DOI":"10.1016\/j.talanta.2020.120710","article-title":"Integrated strategy for accurately screening biomarkers based on metabolomics coupled with network pharmacology","volume":"211","author":"Zhang","year":"2020","journal-title":"Talanta"},{"issue":"21","key":"2021090813432244500_ref273","doi-asserted-by":"crossref","first-page":"13948","DOI":"10.1021\/acs.joc.9b02111","article-title":"Recent changes in the scaffold diversity of organic chemistry as seen in the CAS registry","volume":"84","author":"Lipkus","year":"2019","journal-title":"J Org Chem"},{"issue":"11","key":"2021090813432244500_ref274","doi-asserted-by":"crossref","first-page":"2114","DOI":"10.1021\/acsmedchemlett.0c00319","article-title":"Structural approach to assessing the innovativeness of new drugs finds accelerating rate of innovation","volume":"11","author":"Wills","year":"2020","journal-title":"ACS Med Chem Lett"},{"key":"2021090813432244500_ref275","doi-asserted-by":"crossref","first-page":"D1233","DOI":"10.1093\/nar\/gkaa755","article-title":"INTEDE: interactome of drug-metabolizing enzymes","volume":"49","author":"Yin","year":"2021","journal-title":"Nucleic Acids Res"},{"key":"2021090813432244500_ref276","doi-asserted-by":"crossref","first-page":"D715","DOI":"10.1093\/nar\/gkaa851","article-title":"GIMICA: host genetic and immune factors shaping human microbiota","volume":"49","author":"Tang","year":"2021","journal-title":"Nucleic Acids Res"},{"issue":"4","key":"2021090813432244500_ref277","doi-asserted-by":"crossref","first-page":"633","DOI":"10.1016\/j.immuni.2013.04.005","article-title":"Metabolic pathways in immune cell activation and quiescence","volume":"38","author":"Pearce","year":"2013","journal-title":"Immunity"},{"issue":"D1","key":"2021090813432244500_ref278","doi-asserted-by":"crossref","first-page":"D457","DOI":"10.1093\/nar\/gkv1070","article-title":"KEGG as a reference resource for gene and protein annotation","volume":"44","author":"Kanehisa","year":"2016","journal-title":"Nucleic Acids Res"},{"issue":"D1","key":"2021090813432244500_ref279","doi-asserted-by":"crossref","first-page":"D590","DOI":"10.1093\/nar\/gky962","article-title":"New approach for understanding genome variations in KEGG","volume":"47","author":"Kanehisa","year":"2019","journal-title":"Nucleic Acids Res"},{"issue":"D1","key":"2021090813432244500_ref280","doi-asserted-by":"crossref","first-page":"D353","DOI":"10.1093\/nar\/gkw1092","article-title":"KEGG: new perspectives on genomes, pathways, diseases and drugs","volume":"45","author":"Kanehisa","year":"2017","journal-title":"Nucleic Acids Res"},{"issue":"Suppl 1","key":"2021090813432244500_ref281","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1186\/s12859-018-2016-6","article-title":"BRCA-pathway: a structural integration and visualization system of TCGA breast cancer data on KEGG pathways","volume":"19","author":"Kim","year":"2018","journal-title":"BMC Bioinformatics"},{"issue":"21","key":"2021090813432244500_ref282","doi-asserted-by":"crossref","first-page":"3461","DOI":"10.1093\/bioinformatics\/btx441","article-title":"Reactome enhanced pathway visualization","volume":"33","author":"Sidiropoulos","year":"2017","journal-title":"Bioinformatics"},{"issue":"D1","key":"2021090813432244500_ref283","first-page":"D498","article-title":"The reactome pathway knowledgebase","volume":"48","author":"Jassal","year":"2020","journal-title":"Nucleic Acids Res"},{"issue":"D1","key":"2021090813432244500_ref284","doi-asserted-by":"crossref","first-page":"D649","DOI":"10.1093\/nar\/gkx1132","article-title":"The Reactome pathway knowledgebase","volume":"46","author":"Fabregat","year":"2018","journal-title":"Nucleic Acids Res"},{"issue":"1","key":"2021090813432244500_ref285","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1186\/s12859-017-1559-2","article-title":"Reactome pathway analysis: a high-performance in-memory approach","volume":"18","author":"Fabregat","year":"2017","journal-title":"BMC Bioinformatics"},{"issue":"D1","key":"2021090813432244500_ref286","doi-asserted-by":"crossref","first-page":"D488","DOI":"10.1093\/nar\/gkv1024","article-title":"WikiPathways: capturing the full diversity of pathway knowledge","volume":"44","author":"Kutmon","year":"2016","journal-title":"Nucleic Acids Res"},{"issue":"D1","key":"2021090813432244500_ref287","doi-asserted-by":"crossref","first-page":"D661","DOI":"10.1093\/nar\/gkx1064","article-title":"WikiPathways: a multifaceted pathway database bridging metabolomics to other omics research","volume":"46","author":"Slenter","year":"2018","journal-title":"Nucleic Acids Res"},{"issue":"19\u201320","key":"2021090813432244500_ref288","doi-asserted-by":"crossref","first-page":"851","DOI":"10.1016\/j.drudis.2010.08.002","article-title":"Biotransformation pathway maps in WikiPathways enable direct visualization of drug metabolism related expression changes","volume":"15","author":"Jennen","year":"2010","journal-title":"Drug Discov Today"},{"issue":"D1","key":"2021090813432244500_ref289","doi-asserted-by":"crossref","first-page":"D471","DOI":"10.1093\/nar\/gkv1164","article-title":"The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of pathway\/genome databases","volume":"44","author":"Caspi","year":"2016","journal-title":"Nucleic Acids Res"},{"issue":"D1","key":"2021090813432244500_ref290","doi-asserted-by":"crossref","first-page":"D445","DOI":"10.1093\/nar\/gkz862","article-title":"The MetaCyc database of metabolic pathways and enzymes\u2014a 2019 update","volume":"48","author":"Caspi","year":"2020","journal-title":"Nucleic Acids Res"},{"issue":"9","key":"2021090813432244500_ref291","doi-asserted-by":"crossref","first-page":"1015","DOI":"10.1007\/s00204-011-0705-2","article-title":"A survey of metabolic databases emphasizing the MetaCyc family","volume":"85","author":"Karp","year":"2011","journal-title":"Arch Toxicol"},{"issue":"Database issue","key":"2021090813432244500_ref292","doi-asserted-by":"crossref","first-page":"D480","DOI":"10.1093\/nar\/gkp1002","article-title":"SMPDB: the small molecule pathway database","volume":"38","author":"Frolkis","year":"2010","journal-title":"Nucleic Acids Res"},{"issue":"Database issue","key":"2021090813432244500_ref293","doi-asserted-by":"crossref","first-page":"D478","DOI":"10.1093\/nar\/gkt1067","article-title":"SMPDB 2.0: big improvements to the small molecule pathway database","volume":"42","author":"Jewison","year":"2014","journal-title":"Nucleic Acids Res"},{"issue":"D1","key":"2021090813432244500_ref294","doi-asserted-by":"crossref","first-page":"D90","DOI":"10.1093\/nar\/gkw926","article-title":"miRPathDB: a new dictionary on microRNAs and target pathways","volume":"45","author":"Backes","year":"2017","journal-title":"Nucleic Acids Res"},{"issue":"6","key":"2021090813432244500_ref295","doi-asserted-by":"crossref","first-page":"3114","DOI":"10.1021\/pr401264n","article-title":"Normalyzer: a tool for rapid evaluation of normalization methods for omics data sets","volume":"13","author":"Chawade","year":"2014","journal-title":"J Proteome Res"},{"issue":"6","key":"2021090813432244500_ref296","doi-asserted-by":"crossref","first-page":"399","DOI":"10.1002\/mrc.4238","article-title":"Broadband 1H homodecoupled NMR experiments: recent developments, methods and applications","volume":"53","author":"Castanar","year":"2015","journal-title":"Magn Reson Chem"},{"key":"2021090813432244500_ref297","first-page":"51829","article-title":"Metabolomic analysis of rat brain by high resolution nuclear magnetic resonance spectroscopy of tissue extracts","volume":"91","author":"Lutz","year":"2014","journal-title":"J Vis Exp"},{"issue":"6","key":"2021090813432244500_ref298","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1016\/j.tibtech.2011.02.001","article-title":"NMR-based plant metabolomics: where do we stand, where do we go?","volume":"29","author":"Kim","year":"2011","journal-title":"Trends Biotechnol"},{"issue":"15","key":"2021090813432244500_ref299","doi-asserted-by":"crossref","first-page":"1769","DOI":"10.4155\/bio.11.155","article-title":"Advances in metabolite identification","volume":"3","author":"Wishart","year":"2011","journal-title":"Bioanalysis"},{"key":"2021090813432244500_ref300","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1016\/j.copbio.2016.08.001","article-title":"The future of NMR-based metabolomics","volume":"43","author":"Markley","year":"2017","journal-title":"Curr Opin Biotechnol"},{"issue":"7","key":"2021090813432244500_ref301","doi-asserted-by":"crossref","first-page":"123","DOI":"10.3390\/metabo9070123","article-title":"NMR spectroscopy for metabolomics research","volume":"9","author":"Emwas","year":"2019","journal-title":"Metabolites"},{"key":"2021090813432244500_ref302","doi-asserted-by":"crossref","first-page":"136","DOI":"10.1016\/j.jchromb.2019.04.009","article-title":"Untargeted LC\/MS-based metabolic phenotyping (metabonomics\/metabolomics): the state of the art","volume":"1117","author":"Gika","year":"2019","journal-title":"J Chromatogr B Analyt Technol Biomed Life Sci"},{"issue":"6","key":"2021090813432244500_ref303","doi-asserted-by":"crossref","first-page":"772","DOI":"10.1002\/mas.21562","article-title":"Challenges and emergent solutions for LC\u2013MS\/MS based untargeted metabolomics in diseases","volume":"37","author":"Cui","year":"2018","journal-title":"Mass Spectrom Rev"},{"issue":"2","key":"2021090813432244500_ref304","doi-asserted-by":"crossref","first-page":"470","DOI":"10.1039\/C1MB05350G","article-title":"LC\u2013MS-based metabolomics","volume":"8","author":"Zhou","year":"2012","journal-title":"Mol Biosyst"},{"issue":"8","key":"2021090813432244500_ref305","doi-asserted-by":"crossref","first-page":"1491","DOI":"10.1007\/s00204-014-1234-6","article-title":"LC\u2013MS-based metabolomics: an update","volume":"88","author":"Fang","year":"2014","journal-title":"Arch Toxicol"},{"key":"2021090813432244500_ref306","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1016\/j.copbio.2018.07.010","article-title":"Challenges, progress and promises of metabolite annotation for LC\u2013MS-based metabolomics","volume":"55","author":"Chaleckis","year":"2019","journal-title":"Curr Opin Biotechnol"},{"key":"2021090813432244500_ref307","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1016\/j.jpba.2017.07.013","article-title":"GC-MS based metabolomics used for the identification of cancer volatile organic compounds as biomarkers","volume":"147","author":"Lubes","year":"2018","journal-title":"J Pharm Biomed Anal"},{"issue":"17","key":"2021090813432244500_ref308","doi-asserted-by":"crossref","first-page":"3079","DOI":"10.1039\/C7AN00812K","article-title":"Hyphenated MS-based targeted approaches in metabolomics","volume":"142","author":"Begou","year":"2017","journal-title":"Analyst"},{"issue":"1","key":"2021090813432244500_ref309","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1002\/mas.21553","article-title":"Mass spectrometry-based metabolomics: targeting the crosstalk between gut microbiota and brain in neurodegenerative disorders","volume":"38","author":"Luan","year":"2019","journal-title":"Mass Spectrom Rev"}],"container-title":["Briefings in Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/academic.oup.com\/bib\/article-pdf\/22\/5\/bbab138\/40261851\/bbab138.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"http:\/\/academic.oup.com\/bib\/article-pdf\/22\/5\/bbab138\/40261851\/bbab138.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,28]],"date-time":"2024-08-28T15:35:41Z","timestamp":1724859341000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bib\/article\/doi\/10.1093\/bib\/bbab138\/6236068"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,4,19]]},"references-count":309,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2021,9,2]]}},"URL":"https:\/\/doi.org\/10.1093\/bib\/bbab138","relation":{},"ISSN":["1467-5463","1477-4054"],"issn-type":[{"value":"1467-5463","type":"print"},{"value":"1477-4054","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2021,9]]},"published":{"date-parts":[[2021,4,19]]},"article-number":"bbab138"}}