{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T04:27:01Z","timestamp":1772857621266,"version":"3.50.1"},"reference-count":94,"publisher":"Oxford University Press (OUP)","issue":"W1","license":[{"start":{"date-parts":[[2020,4,23]],"date-time":"2020-04-23T00:00:00Z","timestamp":1587600000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"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"}]},{"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\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["2018QNA7023"],"award-info":[{"award-number":["2018QNA7023"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["10611CDJXZ238826"],"award-info":[{"award-number":["10611CDJXZ238826"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["2018CDQYSG0007"],"award-info":[{"award-number":["2018CDQYSG0007"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["CDJZR14468801"],"award-info":[{"award-number":["CDJZR14468801"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Key R&D Program of Zhejiang Province","award":["2020C03010"],"award-info":[{"award-number":["2020C03010"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,7,2]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Biological processes (like microbial growth\u00a0&amp;\u00a0physiological response) are usually dynamic and require the monitoring of metabolic variation at different time-points. Moreover, there is clear shift from case-control (N=2) study to multi-class (N&amp;gt;2) problem in current metabolomics, which is crucial for revealing the mechanisms underlying certain physiological process, disease metastasis, etc. These time-course and multi-class metabolomics have attracted great attention, and data normalization is essential for removing unwanted biological\/experimental variations in these studies. However, no tool (including NOREVA 1.0 focusing only on case-control studies) is available for effectively assessing the performance of normalization method on time-course\/multi-class metabolomic data. Thus, NOREVA was updated to version 2.0 by (i) realizing normalization and evaluation of both time-course and multi-class metabolomic data, (ii) integrating 144 normalization methods of a recently proposed combination strategy and (iii) identifying the well-performing methods by comprehensively assessing the largest set of normalizations (168 in total, significantly larger than those 24 in NOREVA 1.0). The significance of this update was extensively validated by case studies on benchmark datasets. All in all, NOREVA 2.0 is distinguished for its capability in identifying well-performing normalization method(s) for time-course and multi-class metabolomics, which makes it an indispensable complement to other available tools. NOREVA can be accessed at https:\/\/idrblab.org\/noreva\/.<\/jats:p>","DOI":"10.1093\/nar\/gkaa258","type":"journal-article","created":{"date-parts":[[2020,4,4]],"date-time":"2020-04-04T11:12:39Z","timestamp":1585998759000},"page":"W436-W448","source":"Crossref","is-referenced-by-count":172,"title":["NOREVA: enhanced normalization and evaluation of time-course and multi-class metabolomic data"],"prefix":"10.1093","volume":"48","author":[{"given":"Qingxia","family":"Yang","sequence":"first","affiliation":[{"name":"College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China"},{"name":"School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China"}]},{"given":"Yunxia","family":"Wang","sequence":"first","affiliation":[{"name":"College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China"}]},{"given":"Ying","family":"Zhang","sequence":"first","affiliation":[{"name":"College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China"}]},{"given":"Fengcheng","family":"Li","sequence":"first","affiliation":[{"name":"College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China"}]},{"given":"Weiqi","family":"Xia","sequence":"first","affiliation":[{"name":"College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China"}]},{"given":"Ying","family":"Zhou","sequence":"first","affiliation":[{"name":"Zhejiang Provincial Key Laboratory for Drug Clinical Research and Evaluation\u00a0& The First Affiliated Hospital, Zhejiang University, Hangzhou 310000, China"}]},{"given":"Yunqing","family":"Qiu","sequence":"first","affiliation":[{"name":"Zhejiang Provincial Key Laboratory for Drug Clinical Research and Evaluation\u00a0& The First Affiliated Hospital, Zhejiang University, Hangzhou 310000, China"}]},{"given":"Honglin","family":"Li","sequence":"first","affiliation":[{"name":"School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8069-0053","authenticated-orcid":false,"given":"Feng","family":"Zhu","sequence":"first","affiliation":[{"name":"College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China"},{"name":"School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China"}]}],"member":"286","published-online":{"date-parts":[[2020,4,23]]},"reference":[{"key":"2020062614043480700_B1","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.aca.2019.07.026","article-title":"The effect of sampling procedures and day-to-day variations in metabolomics studies of biofluids","volume":"1081","author":"Giskeodegard","year":"2019","journal-title":"Anal. Chim. Acta"},{"key":"2020062614043480700_B2","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\u00a0Livera","year":"2015","journal-title":"Anal. Chem."},{"key":"2020062614043480700_B3","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1007\/s11306-018-1347-7","article-title":"NormalizeMets: assessing, selecting and implementing statistical methods for normalizing metabolomics data","volume":"14","author":"De\u00a0Livera","year":"2018","journal-title":"Metabolomics"},{"key":"2020062614043480700_B4","doi-asserted-by":"crossref","first-page":"485","DOI":"10.1002\/rcm.7812","article-title":"An efficient data-filtering strategy for easy metabolite detection from the direct analysis of a biological fluid using Fourier transform mass spectrometry","volume":"31","author":"Rathahao-Paris","year":"2017","journal-title":"Rapid Commun. Mass Spectrom."},{"key":"2020062614043480700_B5","doi-asserted-by":"crossref","first-page":"1363","DOI":"10.1021\/acs.analchem.7b04400","article-title":"Best-matched internal standard normalization in liquid chromatography-mass spectrometry metabolomics applied to environmental samples","volume":"90","author":"Boysen","year":"2018","journal-title":"Anal. Chem."},{"key":"2020062614043480700_B6","doi-asserted-by":"crossref","DOI":"10.1093\/bib\/bbz137","article-title":"A novel bioinformatics approach to identify the consistently well-performing normalization strategy for current metabolomic studies","author":"Yang","year":"2019","journal-title":"Brief. Bioinform."},{"key":"2020062614043480700_B7","doi-asserted-by":"crossref","first-page":"2262","DOI":"10.1021\/ac0519312","article-title":"Scaling and normalization effects in NMR spectroscopic metabonomic data sets","volume":"78","author":"Craig","year":"2006","journal-title":"Anal. Chem."},{"key":"2020062614043480700_B8","first-page":"1","article-title":"A systematic evaluation of normalization methods in quantitative label-free proteomics","volume":"19","author":"Valikangas","year":"2018","journal-title":"Brief. Bioinform."},{"key":"2020062614043480700_B9","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."},{"key":"2020062614043480700_B10","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":"2020062614043480700_B11","doi-asserted-by":"crossref","first-page":"633","DOI":"10.1038\/nprot.2017.151","article-title":"Data processing, multi-omic pathway mapping, and metabolite activity analysis using XCMS Online","volume":"13","author":"Forsberg","year":"2018","journal-title":"Nat. Protoc."},{"key":"2020062614043480700_B12","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":"2020062614043480700_B13","doi-asserted-by":"crossref","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","author":"Hong","year":"2019","journal-title":"Brief. Bioinform."},{"key":"2020062614043480700_B14","doi-asserted-by":"crossref","first-page":"732","DOI":"10.1021\/acs.jproteome.8b00523","article-title":"NormalyzerDE: online tool for improved normalization of omics expression data and high-sensitivity differential expression analysis","volume":"18","author":"Willforss","year":"2019","journal-title":"J. Proteome Res."},{"key":"2020062614043480700_B15","doi-asserted-by":"crossref","first-page":"D463","DOI":"10.1093\/nar\/gkv1042","article-title":"Metabolomics Workbench: an international repository for metabolomics data and metadata, metabolite standards, protocols, tutorials and training, and analysis tools","volume":"44","author":"Sud","year":"2016","journal-title":"Nucleic Acids Res."},{"key":"2020062614043480700_B16","article-title":"Consistent gene signature of schizophrenia identified by a novel feature selection strategy from comprehensive sets of transcriptomic data","author":"Yang","year":"2019","journal-title":"Brief. Bioinform."},{"key":"2020062614043480700_B17","doi-asserted-by":"crossref","DOI":"10.1093\/bioinformatics\/btaa022","article-title":"AlpsNMR: an R package for signal processing of fully untargeted NMR-based metabolomics","author":"Madrid-Gambin","year":"2020","journal-title":"Bioinformatics"},{"key":"2020062614043480700_B18","doi-asserted-by":"crossref","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","author":"Hong","year":"2019","journal-title":"Brief. Bioinform."},{"key":"2020062614043480700_B19","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":"2020062614043480700_B20","doi-asserted-by":"crossref","first-page":"giy149","DOI":"10.1093\/gigascience\/giy149","article-title":"PhenoMeNal: processing and analysis of metabolomics data in the cloud","volume":"8","author":"Peters","year":"2019","journal-title":"Gigascience"},{"key":"2020062614043480700_B21","doi-asserted-by":"crossref","first-page":"644","DOI":"10.1126\/science.aav0173","article-title":"Tumor metastasis to lymph nodes requires YAP-dependent metabolic adaptation","volume":"363","author":"Lee","year":"2019","journal-title":"Science"},{"key":"2020062614043480700_B22","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1038\/s41586-018-0353-2","article-title":"Accumulation of succinate controls activation of adipose tissue thermogenesis","volume":"560","author":"Mills","year":"2018","journal-title":"Nature"},{"key":"2020062614043480700_B23","article-title":"Clinical trials, progression-speed differentiating features and swiftness rule of the innovative targets of first-in-class drugs","author":"Li","year":"2019","journal-title":"Brief. Bioinform."},{"key":"2020062614043480700_B24","doi-asserted-by":"crossref","first-page":"597","DOI":"10.1038\/s41556-018-0083-6","article-title":"Cancer-cell-secreted exosomal miR-105 promotes tumour growth through the MYC-dependent metabolic reprogramming of stromal cells","volume":"20","author":"Yan","year":"2018","journal-title":"Nat. Cell Biol."},{"key":"2020062614043480700_B25","doi-asserted-by":"crossref","first-page":"19556","DOI":"10.1074\/jbc.M117.804914","article-title":"Quantitative time-course metabolomics in human red blood cells reveal the temperature dependence of human metabolic networks","volume":"292","author":"Yurkovich","year":"2017","journal-title":"J. Biol. Chem."},{"key":"2020062614043480700_B26","doi-asserted-by":"crossref","first-page":"9779","DOI":"10.1073\/pnas.1808874115","article-title":"Arginine-deprivation-induced oxidative damage sterilizes Mycobacterium tuberculosis","volume":"115","author":"Tiwari","year":"2018","journal-title":"Proc. Natl. Acad. Sci. U.S.A."},{"key":"2020062614043480700_B27","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1038\/s41467-017-02356-9","article-title":"Linking soil biology and chemistry in biological soil crust using isolate exometabolomics","volume":"9","author":"Swenson","year":"2018","journal-title":"Nat. Commun."},{"key":"2020062614043480700_B28","doi-asserted-by":"crossref","first-page":"489","DOI":"10.1016\/j.talanta.2018.04.081","article-title":"Simultaneous detection of multiple inherited metabolic diseases using GC-MS urinary metabolomics by chemometrics multi-class classification strategies","volume":"186","author":"Yang","year":"2018","journal-title":"Talanta"},{"key":"2020062614043480700_B29","doi-asserted-by":"crossref","first-page":"240","DOI":"10.1016\/j.chroma.2019.02.055","article-title":"Revelation of the metabolic pathway of hederacoside C using an innovative data analysis strategy for dynamic multiclass biotransformation experiments","volume":"1595","author":"Peeters","year":"2019","journal-title":"J. Chromatogr. A"},{"key":"2020062614043480700_B30","doi-asserted-by":"crossref","DOI":"10.1016\/j.jmb.2020.01.027","article-title":"SSizer: determining the sample sufficiency for comparative biological study","author":"Li","year":"2020","journal-title":"J. Mol. Biol."},{"key":"2020062614043480700_B31","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1016\/j.aca.2010.01.038","article-title":"Trend analysis of time-series data: a novel method for untargeted metabolite discovery","volume":"663","author":"Peters","year":"2010","journal-title":"Anal. Chim. Acta"},{"key":"2020062614043480700_B32","doi-asserted-by":"crossref","first-page":"10768","DOI":"10.1021\/ac302748b","article-title":"Normalizing and integrating metabolomics data","volume":"84","author":"De\u00a0Livera","year":"2012","journal-title":"Anal. Chem."},{"key":"2020062614043480700_B33","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"},{"key":"2020062614043480700_B34","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1016\/j.biocel.2017.07.002","article-title":"Create, run, share, publish, and reference your LC-MS, FIA-MS, GC-MS, and NMR data analysis workflows with the Workflow4Metabolomics 3.0 Galaxy online infrastructure for metabolomics","volume":"93","author":"Guitton","year":"2017","journal-title":"Int. J. Biochem. Cell Biol."},{"key":"2020062614043480700_B35","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."},{"key":"2020062614043480700_B36","doi-asserted-by":"crossref","first-page":"e1900264","DOI":"10.1002\/pmic.201900264","article-title":"pseudoQC: a regression-based simulation software for correction and normalization of complex metabolomics and proteomics datasets","volume":"19","author":"Wang","year":"2019","journal-title":"Proteomics"},{"key":"2020062614043480700_B37","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1186\/s12859-017-1579-y","article-title":"metaX: a flexible and comprehensive software for processing metabolomics data","volume":"18","author":"Wen","year":"2017","journal-title":"BMC Bioinformatics"},{"key":"2020062614043480700_B38","doi-asserted-by":"crossref","first-page":"72","DOI":"10.3389\/fbioe.2014.00072","article-title":"MetaDB a data processing workflow in untargeted MS-based metabolomics experiments","volume":"2","author":"Franceschi","year":"2014","journal-title":"Front. Bioeng. Biotechnol."},{"key":"2020062614043480700_B39","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1016\/j.aca.2019.08.046","article-title":"Metandem: an online software tool for mass spectrometry-based isobaric labeling metabolomics","volume":"1088","author":"Hao","year":"2019","journal-title":"Anal. Chim. Acta"},{"key":"2020062614043480700_B40","doi-asserted-by":"crossref","first-page":"2870","DOI":"10.1093\/bioinformatics\/bty1066","article-title":"MetFlow: an interactive and integrated workflow for metabolomics data cleaning and differential metabolite discovery","volume":"35","author":"Shen","year":"2019","journal-title":"Bioinformatics"},{"key":"2020062614043480700_B41","doi-asserted-by":"crossref","first-page":"E237","DOI":"10.3390\/metabo9100237","article-title":"WebSpecmine: a website for metabolomics data analysis and mining","volume":"9","author":"Cardoso","year":"2019","journal-title":"Metabolites"},{"key":"2020062614043480700_B42","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":"2020062614043480700_B43","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":"2020062614043480700_B44","doi-asserted-by":"crossref","first-page":"1357","DOI":"10.1021\/ac8019366","article-title":"Development of a robust and repeatable UPLC-MS method for the long-term metabolomic study of human serum","volume":"81","author":"Zelena","year":"2009","journal-title":"Anal. Chem."},{"key":"2020062614043480700_B45","doi-asserted-by":"crossref","first-page":"5132","DOI":"10.1021\/pr900499r","article-title":"Analytical error reduction using single point calibration for accurate and precise metabolomic phenotyping","volume":"8","author":"van\u00a0der\u00a0Kloet","year":"2009","journal-title":"J. Proteome Res."},{"key":"2020062614043480700_B46","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1007\/s11306-016-1124-4","article-title":"Large-scale untargeted LC-MS metabolomics data correction using between-batch feature alignment and cluster-based within-batch signal intensity drift correction","volume":"12","author":"Brunius","year":"2016","journal-title":"Metabolomics"},{"key":"2020062614043480700_B47","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1016\/j.aca.2016.12.029","article-title":"Metabolomic analysis of urine samples by UHPLC-QTOF-MS: impact of normalization strategies","volume":"955","author":"Gagnebin","year":"2017","journal-title":"Anal. Chim. Acta"},{"key":"2020062614043480700_B48","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."},{"key":"2020062614043480700_B49","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1111\/j.1541-0420.2008.01057.x","article-title":"On gene ranking using replicated microarray time course data","volume":"65","author":"Tai","year":"2009","journal-title":"Biometrics"},{"key":"2020062614043480700_B50","doi-asserted-by":"crossref","first-page":"1865","DOI":"10.1002\/fsn3.1042","article-title":"Untargeted metabolomics analysis of Mucorracemosus Douchi fermentation process by gas chromatography with time-of-flight mass spectrometry","volume":"7","author":"Li","year":"2019","journal-title":"Food Sci. Nutr."},{"key":"2020062614043480700_B51","doi-asserted-by":"crossref","first-page":"3322","DOI":"10.1021\/acs.jproteome.5b00354","article-title":"Analysis of the human adult urinary metabolome variations with age, body mass index, and gender by implementing a comprehensive workflow for univariate and OPLS statistical analyses","volume":"14","author":"Thevenot","year":"2015","journal-title":"J. Proteome Res."},{"key":"2020062614043480700_B52","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1007\/s11306-018-1402-4","article-title":"Oxylipin profiling of human plasma reflects the renal dysfunction in uremic patients","volume":"14","author":"Hu","year":"2018","journal-title":"Metabolomics"},{"key":"2020062614043480700_B53","doi-asserted-by":"crossref","first-page":"2376","DOI":"10.3390\/molecules23092376","article-title":"Characterization of cultivar differences of blueberry wines using GC-QTOF-MS and metabolic profiling methods","volume":"23","author":"Yuan","year":"2018","journal-title":"Molecules"},{"key":"2020062614043480700_B54","doi-asserted-by":"crossref","first-page":"911","DOI":"10.2337\/dc16-2453","article-title":"Targeted metabolomics demonstrates distinct and overlapping maternal metabolites associated with BMI, glucose, and insulin sensitivity during pregnancy across four ancestry groups","volume":"40","author":"Jacob","year":"2017","journal-title":"Diabetes Care."},{"key":"2020062614043480700_B55","doi-asserted-by":"crossref","first-page":"3684","DOI":"10.1093\/bioinformatics\/bty390","article-title":"Single cell clustering based on cell-pair differentiability correlation and variance analysis","volume":"34","author":"Jiang","year":"2018","journal-title":"Bioinformatics"},{"key":"2020062614043480700_B56","doi-asserted-by":"crossref","first-page":"e90109","DOI":"10.1371\/journal.pone.0090109","article-title":"A formal algorithm for verifying the validity of clustering results based on model checking","volume":"9","author":"Huang","year":"2014","journal-title":"PLoS One"},{"key":"2020062614043480700_B57","first-page":"49","article-title":"Similarity measures for text document clustering","volume":"2008","author":"Huang","year":"2008","journal-title":"Proc. N Z Comput. Sci. Res. Stud. Conf."},{"key":"2020062614043480700_B58","doi-asserted-by":"crossref","first-page":"1235","DOI":"10.1039\/C4MB00711E","article-title":"Optimal consistency in microRNA expression analysis using reference-gene-based normalization","volume":"11","author":"Wang","year":"2015","journal-title":"Mol. Biosyst."},{"key":"2020062614043480700_B59","doi-asserted-by":"crossref","first-page":"671","DOI":"10.1016\/S0140-6736(05)17947-1","article-title":"Gene-expression profiles to predict distant metastasis of lymph-node-negative primary breast cancer","volume":"365","author":"Wang","year":"2005","journal-title":"Lancet"},{"key":"2020062614043480700_B60","doi-asserted-by":"crossref","first-page":"1899","DOI":"10.1212\/WNL.0000000000007313","article-title":"Large-scale plasma metabolome analysis reveals alterations in HDL metabolism in migraine","volume":"92","author":"Onderwater","year":"2019","journal-title":"Neurology"},{"key":"2020062614043480700_B61","doi-asserted-by":"crossref","first-page":"505","DOI":"10.4103\/0019-5154.190118","article-title":"Methodology series module 5: sampling strategies","volume":"61","author":"Setia","year":"2016","journal-title":"Indian J. Dermatol."},{"key":"2020062614043480700_B62","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1038\/nmeth.3252","article-title":"Orchestrating high-throughput genomic analysis with Bioconductor","volume":"12","author":"Huber","year":"2015","journal-title":"Nat. Methods"},{"key":"2020062614043480700_B63","doi-asserted-by":"crossref","first-page":"1921","DOI":"10.1109\/TPAMI.2010.34","article-title":"Evaluating stability and comparing output of feature selectors that optimize feature subset cardinality","volume":"32","author":"Somol","year":"2010","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"2020062614043480700_B64","first-page":"D440","article-title":"MetaboLights: a resource evolving in response to the needs of its scientific community","volume":"48","author":"Haug","year":"2020","journal-title":"Nucleic Acids Res."},{"key":"2020062614043480700_B65","doi-asserted-by":"crossref","first-page":"242","DOI":"10.1093\/jamia\/ocy165","article-title":"Robust clinical marker identification for diabetic kidney disease with ensemble feature selection","volume":"26","author":"Song","year":"2019","journal-title":"J. Am. Med. Inform. Assoc."},{"key":"2020062614043480700_B66","doi-asserted-by":"crossref","first-page":"896","DOI":"10.1038\/nbt.2931","article-title":"Normalization of RNA-seq data using factor analysis of control genes or samples","volume":"32","author":"Risso","year":"2014","journal-title":"Nat. Biotechnol."},{"key":"2020062614043480700_B67","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1080\/10618600.1996.10474713","article-title":"R: a language for data analysis and graphics","volume":"5","author":"Ihaka","year":"1995","journal-title":"J. Comput. Graph. Stat."},{"key":"2020062614043480700_B68","doi-asserted-by":"crossref","first-page":"13678","DOI":"10.1038\/s41598-017-14070-z","article-title":"Controlling the overfitting of heritability in genomic selection through cross validation","volume":"7","author":"Jia","year":"2017","journal-title":"Sci. Rep."},{"key":"2020062614043480700_B69","doi-asserted-by":"crossref","first-page":"951","DOI":"10.1093\/bioinformatics\/btw771","article-title":"Feature selection using a one dimensional naive Bayes' classifier increases the accuracy of support vector machine classification of CDR3 repertoires","volume":"33","author":"Cinelli","year":"2017","journal-title":"Bioinformatics"},{"key":"2020062614043480700_B70","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1080\/10245332.2017.1385211","article-title":"EgoNet identifies differential ego-modules and pathways related to prednisolone resistance in childhood acute lymphoblastic leukemia","volume":"23","author":"Jiang","year":"2018","journal-title":"Hematology"},{"key":"2020062614043480700_B71","doi-asserted-by":"crossref","first-page":"1860","DOI":"10.1093\/bioinformatics\/btr288","article-title":"The role of indirect connections in gene networks in predicting function","volume":"27","author":"Gillis","year":"2011","journal-title":"Bioinformatics"},{"key":"2020062614043480700_B72","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1002\/cem.1420","article-title":"A benchmark spike-in data set for biomarker identification in metabolomics","volume":"26","author":"Franceschi","year":"2012","journal-title":"J. Chemom."},{"key":"2020062614043480700_B73","doi-asserted-by":"crossref","first-page":"158","DOI":"10.1016\/j.redox.2018.04.011","article-title":"Integrative metabolomics and transcriptomics signatures of clinical tolerance to Plasmodium vivax reveal activation of innate cell immunity and T cell signaling","volume":"17","author":"Gardinassi","year":"2018","journal-title":"Redox. Biol."},{"key":"2020062614043480700_B74","doi-asserted-by":"crossref","first-page":"54","DOI":"10.3389\/fcell.2014.00054","article-title":"Comparative transcriptomics and metabolomics in a Rhesus macaque drug administration study","volume":"2","author":"Lee","year":"2014","journal-title":"Front. Cell Dev. Biol."},{"key":"2020062614043480700_B75","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1007\/s11306-016-1054-1","article-title":"Metabolic analysis of the response of Pseudomonas putida DOT-T1E strains to toluene using Fourier transform infrared spectroscopy and gas chromatography mass spectrometry","volume":"12","author":"Sayqal","year":"2016","journal-title":"Metabolomics"},{"key":"2020062614043480700_B76","doi-asserted-by":"crossref","first-page":"17141","DOI":"10.1038\/s41598-017-17362-6","article-title":"A pilot characterization of the human chronobiome","volume":"7","author":"Skarke","year":"2017","journal-title":"Sci. Rep."},{"key":"2020062614043480700_B77","doi-asserted-by":"crossref","first-page":"9105","DOI":"10.1002\/ece3.4361","article-title":"Seasonal variation of secondary metabolites in nine different bryophytes","volume":"8","author":"Peters","year":"2018","journal-title":"Ecol. Evol."},{"key":"2020062614043480700_B78","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1007\/s11306-016-1134-2","article-title":"A novel targeted\/untargeted GC-Orbitrap metabolomics methodology applied to Candidaalbicans and Staphylococcus aureus biofilms","volume":"12","author":"Weidt","year":"2016","journal-title":"Metabolomics"},{"key":"2020062614043480700_B79","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."},{"key":"2020062614043480700_B80","doi-asserted-by":"crossref","first-page":"e12689","DOI":"10.1111\/pim.12689","article-title":"Kynurenine elevation correlates with T regulatory cells increase in acute Plasmodium vivax infection: a pilot study","volume":"42","author":"Dos\u00a0Santos","year":"2019","journal-title":"Parasite Immunol."},{"key":"2020062614043480700_B81","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1016\/j.neuropharm.2016.02.029","article-title":"The kynurenine pathway and parasitic infections that affect CNS function","volume":"112","author":"Hunt","year":"2017","journal-title":"Neuropharmacology"},{"key":"2020062614043480700_B82","doi-asserted-by":"crossref","first-page":"209","DOI":"10.3389\/fmicb.2015.00209","article-title":"Diel metabolomics analysis of a hot spring chlorophototrophic microbial mat leads to new hypotheses of community member metabolisms","volume":"6","author":"Kim","year":"2015","journal-title":"Front. Microbiol."},{"key":"2020062614043480700_B83","doi-asserted-by":"crossref","first-page":"E1565","DOI":"10.3390\/ijms17091565","article-title":"Plant-to-plant variability in root metabolite profiles of 19 Arabidopsis thaliana accessions is substance-class-dependent","volume":"17","author":"Monchgesang","year":"2016","journal-title":"Int. J. Mol. Sci."},{"key":"2020062614043480700_B84","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/j.foodres.2019.02.021","article-title":"Highly geographical specificity of metabolomic traits among Korean domestic soybeans (Glycine max)","volume":"120","author":"Lee","year":"2019","journal-title":"Food Res. Int."},{"key":"2020062614043480700_B85","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"},{"key":"2020062614043480700_B86","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1111\/tpj.14320","article-title":"A comprehensive time-course metabolite profiling of the model cyanobacterium Synechocystissp. PCC 6803 under diurnal light:dark cycles","volume":"99","author":"Werner","year":"2019","journal-title":"Plant J."},{"key":"2020062614043480700_B87","article-title":"ANPELA: analysis and performance assessment of the label-free quantification workflow for metaproteomic studies","author":"Tang","year":"2019","journal-title":"Brief. Bioinform."},{"key":"2020062614043480700_B88","doi-asserted-by":"crossref","first-page":"483","DOI":"10.1007\/s00216-017-0738-3","article-title":"Potential of dynamically harmonized Fourier transform ion cyclotron resonance cell for high-throughput metabolomics fingerprinting: control of data quality","volume":"410","author":"Habchi","year":"2018","journal-title":"Anal. Bioanal. Chem."},{"key":"2020062614043480700_B89","doi-asserted-by":"crossref","first-page":"1041","DOI":"10.1093\/annonc\/mdv179","article-title":"Prioritizing precision medicine for prostate cancer","volume":"26","author":"Attard","year":"2015","journal-title":"Ann. Oncol."},{"key":"2020062614043480700_B90","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":"2020062614043480700_B91","doi-asserted-by":"crossref","DOI":"10.1080\/19490976.2020.1712984","article-title":"Lactobacillus and Pediococcus ameliorate progression of non-alcoholic fatty liver disease through modulation of the gut microbiome","author":"Lee","year":"2020","journal-title":"Gut Microbes"},{"key":"2020062614043480700_B92","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":"2020062614043480700_B93","doi-asserted-by":"crossref","first-page":"1128","DOI":"10.1021\/acschemneuro.7b00490","article-title":"What contributes to serotonin-norepinephrine reuptake inhibitors' 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. Neurosci."},{"key":"2020062614043480700_B94","doi-asserted-by":"crossref","first-page":"E121","DOI":"10.3390\/microorganisms7050121","article-title":"Role of gut microbiota in hepatocarcinogenesis","volume":"7","author":"Gupta","year":"2019","journal-title":"Microorganisms"}],"container-title":["Nucleic Acids Research"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/academic.oup.com\/nar\/article-pdf\/48\/W1\/W436\/33433419\/gkaa258.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"http:\/\/academic.oup.com\/nar\/article-pdf\/48\/W1\/W436\/33433419\/gkaa258.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,3]],"date-time":"2024-08-03T12:18:01Z","timestamp":1722687481000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/nar\/article\/48\/W1\/W436\/5824156"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,4,23]]},"references-count":94,"journal-issue":{"issue":"W1","published-online":{"date-parts":[[2020,4,23]]},"published-print":{"date-parts":[[2020,7,2]]}},"URL":"https:\/\/doi.org\/10.1093\/nar\/gkaa258","relation":{},"ISSN":["0305-1048","1362-4962"],"issn-type":[{"value":"0305-1048","type":"print"},{"value":"1362-4962","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2020,7,2]]},"published":{"date-parts":[[2020,4,23]]}}}