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pubmed-article:18831782pubmed:abstractTextArray-based comparative genomic hybridization (array CGH) is a highly efficient technique, allowing the simultaneous measurement of genomic DNA copy number at hundreds or thousands of loci and the reliable detection of local one-copy-level variations. Characterization of these DNA copy number changes is important for both the basic understanding of cancer and its diagnosis. In order to develop effective methods to identify aberration regions from array CGH data, many recent research work focus on both smoothing-based and segmentation-based data processing. In this paper, we propose stationary packet wavelet transform based approach to smooth array CGH data. Our purpose is to remove CGH noise in whole frequency while keeping true signal by using bivariate model.lld:pubmed
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pubmed-article:18831782pubmed:authorpubmed-author:BonE PEPlld:pubmed
pubmed-article:18831782pubmed:authorpubmed-author:HuangHengHlld:pubmed
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pubmed-article:18831782pubmed:authorpubmed-author:NguyenNhaNlld:pubmed
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pubmed-article:18831782pubmed:volume9 Suppl 2lld:pubmed
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pubmed-article:18831782pubmed:dateRevised2009-11-18lld:pubmed
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pubmed-article:18831782pubmed:articleTitleArray CGH data modeling and smoothing in Stationary Wavelet Packet Transform domain.lld:pubmed
pubmed-article:18831782pubmed:affiliationDepartment of Computer Science and Engineering, University of Texas at Arlington, TX, USA. heng@uta.edulld:pubmed
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pubmed-article:18831782pubmed:publicationTypeComparative Studylld:pubmed
pubmed-article:18831782pubmed:publicationTypeResearch Support, Non-U.S. Gov'tlld:pubmed