{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,15]],"date-time":"2026-01-15T05:44:33Z","timestamp":1768455873251,"version":"3.49.0"},"reference-count":35,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2024,5,10]],"date-time":"2024-05-10T00:00:00Z","timestamp":1715299200000},"content-version":"vor","delay-in-days":6553,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-sa\/3.0\/"}],"funder":[{"DOI":"10.13039\/100008594","name":"European Hematology Association","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100008594","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Jos\u00e9 Carreras Foundation"},{"DOI":"10.13039\/501100022300","name":"Institut des Hautes Etudes Scientifiques","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100022300","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100004794","name":"Centre National de la Recherche Scientifique","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100004794","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Institut National de la Sant\u00e9 Et de la Recherche M\u00e9dicale"},{"name":"French Ministry of Research"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2006,6,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Studies on high-throughput global gene expression using microarray technology have generated ever larger amounts of systematic transcriptome data. A major challenge in exploiting these heterogeneous datasets is how to normalize the expression profiles by inter-assay methods. Different non-linear and linear normalization methods have been developed, which essentially rely on the hypothesis that the true or perceived logarithmic fold-change distributions between two different assays are symmetric in nature. However, asymmetric gene expression changes are frequently observed, leading to suboptimal normalization results and in consequence potentially to thousands of false calls. Therefore, we have specifically investigated asymmetric comparative transcriptome profiles and developed the normalization using weighted negative second order exponential error functions (NeONORM) for robust and global inter-assay normalization. NeONORM efficiently damps true gene regulatory events in order to minimize their misleading impact on the normalization process. We evaluated NeONORM\u2019s applicability using artificial and true experimental datasets, both of which demonstrated that NeONORM could be systematically applied to inter-assay and inter-condition comparisons.<\/jats:p>","DOI":"10.1016\/s1672-0229(06)60021-1","type":"journal-article","created":{"date-parts":[[2006,8,25]],"date-time":"2006-08-25T22:06:51Z","timestamp":1156543611000},"page":"90-109","source":"Crossref","is-referenced-by-count":28,"title":["Normalization Using Weighted Negative Second Order Exponential Error Functions (NeONORM) Provides Robustness Against Asymmetries in Comparative Transcriptome Profiles and Avoids False Calls"],"prefix":"10.1093","volume":"4","author":[{"given":"Sebastian","family":"Noth","sequence":"first","affiliation":[{"name":"Systems Epigenomics Group, Institut des Hautes Etudes Scientifiques\/Institut de Recherches Interdisciplinaires, CNRS\/INSERM , Bures sur Yvette, 91440 , France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guillaume","family":"Brysbaert","sequence":"additional","affiliation":[{"name":"Systems Epigenomics Group, Institut des Hautes Etudes Scientifiques\/Institut de Recherches Interdisciplinaires, CNRS\/INSERM , Bures sur Yvette, 91440 , France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Arndt","family":"Benecke","sequence":"additional","affiliation":[{"name":"Systems Epigenomics Group, Institut des Hautes Etudes Scientifiques\/Institut de Recherches Interdisciplinaires, CNRS\/INSERM , Bures sur Yvette, 91440 , France"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2006,8,22]]},"reference":[{"key":"2024051008231215100_bib1","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1146\/annurev.biochem.74.082803.133212","article-title":"Applications of DNA microarrays in biology","volume":"74","author":"Stoughton","year":"2005","journal-title":"Annu. Rev. Biochem."},{"key":"2024051008231215100_bib2","doi-asserted-by":"crossref","first-page":"126","DOI":"10.1186\/1479-7364-2-2-126","article-title":"The genetics of regulatory variation in the human genome","volume":"2","author":"Stranger","year":"2005","journal-title":"Hum. Genomics"},{"key":"2024051008231215100_bib3","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1186\/bcr1018","article-title":"The promise of microarrays in the management and treatment of breast cancer","volume":"7","author":"Chang","year":"2005","journal-title":"Breast Cancer Res."},{"key":"2024051008231215100_bib4","doi-asserted-by":"crossref","first-page":"312","DOI":"10.1081\/CNV-120030219","article-title":"Impact of microarray technology in clinical oncology","volume":"22","author":"Raetz","year":"2004","journal-title":"Cancer Invest."},{"key":"2024051008231215100_bib5","doi-asserted-by":"crossref","first-page":"S18","DOI":"10.1038\/ng1559","article-title":"Mapping of genetic and epigenetic regulatory networks using microarrays","volume":"37","author":"van Steensel","year":"2005","journal-title":"Nat. Genet."},{"key":"2024051008231215100_bib6","doi-asserted-by":"crossref","first-page":"649","DOI":"10.1016\/j.tig.2003.09.015","article-title":"Fundamentals of cDNA microarray data analysis","volume":"19","author":"Leung","year":"2003","journal-title":"Trends Genet."},{"key":"2024051008231215100_bib7","doi-asserted-by":"crossref","first-page":"701","DOI":"10.1111\/j.0006-341X.2002.00701.x","article-title":"DNA microarray experiments: biological and technological aspects","volume":"58","author":"Nguyen","year":"2002","journal-title":"Biometrics"},{"key":"2024051008231215100_bib8","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1038\/4447","article-title":"High density synthetic oligonucleotides arrays","volume":"21","author":"Lipschutz","year":"1999","journal-title":"Nat. Genet."},{"key":"2024051008231215100_bib9","doi-asserted-by":"crossref","first-page":"819","DOI":"10.1089\/10665270050514954","article-title":"Analysis of variance for gene expression microarray data","volume":"7","author":"Kerr","year":"2000","journal-title":"J. Comput. Biol."},{"key":"2024051008231215100_bib10","doi-asserted-by":"crossref","first-page":"1325","DOI":"10.1093\/bioinformatics\/btg146","article-title":"New normalization methods for cDNA microarray data","volume":"19","author":"Wilson","year":"2003","journal-title":"Bioinformatics"},{"key":"2024051008231215100_bib11","article-title":"Normalization for cDNA microarray data","volume-title":"Optical Technologies and Informatics","author":"Yang","year":"2001"},{"key":"2024051008231215100_bib12","doi-asserted-by":"crossref","first-page":"194","DOI":"10.1186\/1471-2105-5-194","article-title":"Optimized LOWESS normalization parameter selection for DNA microarray data","volume":"5","author":"Berger","year":"2004","journal-title":"BMC Bioinformatics"},{"key":"2024051008231215100_bib13","doi-asserted-by":"crossref","DOI":"10.1186\/gb-2002-3-9-research0048","article-title":"A new non-linear normalization method for reducing variability in DNA microarray experiments","volume":"3","author":"Workman","year":"2002","journal-title":"Genome Biol."},{"key":"2024051008231215100_bib14","doi-asserted-by":"crossref","first-page":"e15","DOI":"10.1093\/nar\/30.4.e15","article-title":"Normalization for cDNA microarray data: a robust composite method addressing single and multiple slide systematic variation","volume":"30","author":"Yang","year":"2002","journal-title":"Nucleic Acids Res."},{"key":"2024051008231215100_bib15","doi-asserted-by":"crossref","first-page":"803","DOI":"10.1016\/j.bone.2005.02.001","article-title":"Microarray analysis reveals expression regulation of Wnt antagonists in differentiating osteoblasts","volume":"36","author":"Vaes","year":"2005","journal-title":"Bone"},{"key":"2024051008231215100_bib16","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1093\/bioinformatics\/bth469","article-title":"Outcome signature genes in breast cancer: is there a unique set?","volume":"21","author":"Ein-Dor","year":"2005","journal-title":"Bioinformatics"},{"key":"2024051008231215100_bib17","doi-asserted-by":"crossref","first-page":"591","DOI":"10.1016\/S1097-2765(01)00325-2","article-title":"Identification of hTAF(II)80 delta links apoptotic signaling pathways to transcription factor TFIID function","volume":"8","author":"Bell","year":"2001","journal-title":"Mol. Cell"},{"key":"2024051008231215100_bib18","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1016\/S0968-0004(97)01045-1","article-title":"Mitotic repression of the transcriptional machinery","volume":"22","author":"Gottesfeld","year":"1997","journal-title":"Trends Biochem. Sci."},{"key":"2024051008231215100_bib19","doi-asserted-by":"crossref","first-page":"1794","DOI":"10.1126\/science.1110324","article-title":"Early asymmetry of gene transcription in embryonic human left and right cerebral cortex","volume":"308","author":"Sun","year":"2005","journal-title":"Science"},{"key":"2024051008231215100_bib20","doi-asserted-by":"crossref","first-page":"1413","DOI":"10.1016\/j.bbrc.2004.09.207","article-title":"Foxjl regulates asymmetric gene expression during left-right axis patterning in mice","volume":"324","author":"Zhang","year":"2004","journal-title":"Biochem. Biophys. Res. Commun."},{"key":"2024051008231215100_bib21","doi-asserted-by":"crossref","first-page":"735","DOI":"10.1093\/bioinformatics\/18.5.735","article-title":"Adaptive quality-based clustering of gene expression profiles","volume":"18","author":"De Smet","year":"2002","journal-title":"Bioinformatics"},{"key":"2024051008231215100_bib22","doi-asserted-by":"crossref","first-page":"148","DOI":"10.1186\/1471-2105-5-148","article-title":"Rank Difference Analysis of Microarrays (RDAM), a novel approach to statistical analysis of microarray expression profiling data","volume":"5","author":"Martin","year":"2004","journal-title":"BMC Bioinformatics"},{"key":"2024051008231215100_bib23","doi-asserted-by":"crossref","first-page":"1956","DOI":"10.1126\/science.1090022","article-title":"A gene expression map of the Arabidopsis root","volume":"302","author":"Birnbaum","year":"2003","journal-title":"Science"},{"key":"2024051008231215100_bib24","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"},{"key":"2024051008231215100_bib25","doi-asserted-by":"crossref","first-page":"546","DOI":"10.1093\/bioinformatics\/18.4.546","article-title":"A comparative review of statistical methods for discovering differentially expressed genes in replicated microarray experiments","volume":"18","author":"Pan","year":"2002","journal-title":"Bioinformatics"},{"key":"2024051008231215100_bib26","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1186\/1471-2105-5-121","article-title":"SED, a normalization free method for DNA microarray data analysis","volume":"5","author":"Wang","year":"2004","journal-title":"BMC Bioinformatics"},{"key":"2024051008231215100_bib27","doi-asserted-by":"crossref","first-page":"673","DOI":"10.1038\/89044","article-title":"Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks","volume":"7","author":"Khan","year":"2001","journal-title":"Nat. Med."},{"key":"2024051008231215100_bib28","doi-asserted-by":"crossref","first-page":"1080","DOI":"10.1182\/blood.V77.5.1080.1080","article-title":"NB4, a maturation inducible cell line with t(15;17) marker isolated from a human acute promyelocytic leukemia","volume":"77","author":"Lanotte","year":"1991","journal-title":"Blood"},{"key":"2024051008231215100_bib29","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1006\/scel.1994.1015","article-title":"The retinoid signaling pathway: molecular and genetic analyses","volume":"5","author":"Chambon","year":"1994","journal-title":"Semin. Cell Biol."},{"key":"2024051008231215100_bib30","doi-asserted-by":"crossref","first-page":"835","DOI":"10.1016\/0092-8674(95)90199-X","article-title":"The nuclear receptor superfamily: the second decade","volume":"83","author":"Mangelsdorf","year":"1995","journal-title":"Cell"},{"key":"2024051008231215100_bib31","doi-asserted-by":"crossref","first-page":"1079","DOI":"10.1126\/science.1065173","article-title":"Methyltransferase recruitment and DNA hypermethylation of target promoters by an oncogenic transcription factor","volume":"295","author":"Di Croce","year":"2002","journal-title":"Science"},{"key":"2024051008231215100_bib32","first-page":"111","article-title":"Statistical methods for identifying differentially expressed genes in replicated cDNA microarray experiments","volume":"12","author":"Dudoit","year":"2002","journal-title":"Statistica Sinica"},{"key":"2024051008231215100_bib33","doi-asserted-by":"crossref","first-page":"829","DOI":"10.1080\/01621459.1979.10481038","article-title":"Robust locally weighted regression and smoothing scatterplots","volume":"74","author":"Cleveland","year":"1979","journal-title":"J. Amer. Statist. Assoc."},{"key":"2024051008231215100_bib34","doi-asserted-by":"crossref","first-page":"596","DOI":"10.1080\/01621459.1988.10478639","article-title":"Locally weighted regression: an approach to regression analysis by local fitting","volume":"83","author":"Cleveland","year":"1988","journal-title":"J. Amer. Statist. Assoc."},{"key":"2024051008231215100_bib35","doi-asserted-by":"crossref","first-page":"5468","DOI":"10.1074\/jbc.M413040200","article-title":"ASB2 is an Elongin BC-interacting protein that can assemble with Cullin 5 and Rbx1 to reconstitute an E3 ubiquitin ligase complex","volume":"280","author":"Heuze","year":"2005","journal-title":"J. Biol. Chem."}],"container-title":["Genomics, Proteomics &amp; Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1672022906600211?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1672022906600211?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/academic.oup.com\/gpb\/article-pdf\/4\/2\/90\/57482757\/gpb_4_2_90.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/gpb\/article-pdf\/4\/2\/90\/57482757\/gpb_4_2_90.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,5,10]],"date-time":"2024-05-10T08:23:51Z","timestamp":1715329431000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/gpb\/article\/4\/2\/90\/7218767"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2006,6,1]]},"references-count":35,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2006,6,1]]}},"URL":"https:\/\/doi.org\/10.1016\/s1672-0229(06)60021-1","relation":{},"ISSN":["1672-0229","2210-3244"],"issn-type":[{"value":"1672-0229","type":"print"},{"value":"2210-3244","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2006,6]]},"published":{"date-parts":[[2006,6,1]]}}}