{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,19]],"date-time":"2025-10-19T18:13:15Z","timestamp":1760897595671},"reference-count":13,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2005,2,11]],"date-time":"2005-02-11T00:00:00Z","timestamp":1108080000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/2.0\/"},{"start":{"date-parts":[[2005,2,11]],"date-time":"2005-02-11T00:00:00Z","timestamp":1108080000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/2.0\/"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Bioinformatics"],"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                        <jats:title>Background<\/jats:title>\n                        <jats:p>Normalization is a critical step in analysis of gene expression profiles. For dual-labeled arrays, global normalization assumes that the majority of the genes on the array are non-differentially expressed between the two channels and that the number of over-expressed genes approximately equals the number of under-expressed genes. These assumptions can be inappropriate for custom arrays or arrays in which the reference RNA is very different from the experimental samples.<\/jats:p>\n                     <\/jats:sec><jats:sec>\n                        <jats:title>Results<\/jats:title>\n                        <jats:p>We propose a mixture model based normalization method that adaptively identifies non-differentially expressed genes and thereby substantially improves normalization for dual-labeled arrays in settings where the assumptions of global normalization are problematic. The new method is evaluated using both simulated and real data.<\/jats:p>\n                     <\/jats:sec><jats:sec>\n                        <jats:title>Conclusions<\/jats:title>\n                        <jats:p>The new normalization method is effective for general microarray platforms when samples with very different expression profile are co-hybridized and for custom arrays where the majority of genes are likely to be differentially expressed.<\/jats:p>\n                     <\/jats:sec>","DOI":"10.1186\/1471-2105-6-28","type":"journal-article","created":{"date-parts":[[2005,2,12]],"date-time":"2005-02-12T07:25:26Z","timestamp":1108193126000},"update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["An adaptive method for cDNA microarray normalization"],"prefix":"10.1186","volume":"6","author":[{"given":"Yingdong","family":"Zhao","sequence":"first","affiliation":[]},{"given":"Ming-Chung","family":"Li","sequence":"additional","affiliation":[]},{"given":"Richard","family":"Simon","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2005,2,11]]},"reference":[{"key":"353_CR1","doi-asserted-by":"crossref","unstructured":"Zien A, Aigner T, Zimmer R, Lengauer T: Centralization: a new method for the normalization of gene expression data. 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