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Wavelet Based Array Comparative Genomic Hybridization And Mass Spectrometry Data Analysis

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Wavelet Based Array Comparative Genomic Hybridization And Mass Spectrometry Data Analysis

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dc.contributor.author Nguyen, Nha en_US
dc.date.accessioned 2010-11-01T21:28:54Z
dc.date.available 2010-11-01T21:28:54Z
dc.date.issued 2010-11-01
dc.date.submitted January 2010 en_US
dc.identifier.other DISS-10752 en_US
dc.identifier.uri http://hdl.handle.net/10106/5137
dc.description.abstract As a highly efficient technique, array-based comparative genomic hybridization (aCGH) methods allow the simultaneous measurement of genomic DNA copy number at hundreds or thousands of loci and the reliable detection of local one-copy-level variations. The identification of these DNA copy number changes provides insights to facilitate both the basic understanding of cancer and its diagnosis. In order to effectively analyze aCGH data, various techniques have been proposed to help researchers smooth the DNA copy number data and subsequently to quantify the alterations. In this thesis, many wavelet based methods are proposed to smooth and segment the aCGH data that is the key step to detect DNA copy number alterations. The proposed smooth methods are combinations of shift-invariant wavelet transforms ( such as dual tree complex wavelet transform and stationary wavelet packet transform) and bivariate shrinkage estimators. The proposed segmentation method includes two main steps such as heavy-tailed noise suppression and derivative wavelet scalogram based segmentation. The proposed method is performed on both synthetic and real datasets. The experimental results show that proposed method outperforms the previous approaches.Mass Spectrometry (MS) is increasingly being used to discover diseases related proteomic patterns. The smooth and peak detection steps are important steps in the typical analysis of MS data. Recently, many new algorithms have been proposed to increase true position rate with low false discovery rate in peak detection. In this thesis, two peak detection methods are proposed. The first proposed method is GaborEnvelop method which is a combination of Gabor filters and envelope analysis. The second proposed method is GDWavelet method which is used to process mass spectrometry based on Gaussian derivative wavelet. Both the proposed methods can detect more true peaks with a lower false discovery rate than previous methods. The proposed methods have been performed on the real SELDI-TOF spectrum with known polypeptide positions and on two synthetic data with Gaussian and real noise. The experimental results demonstrate the proposed methods outperform other common used methods in the Receiver Operating Characteristic (ROC) curve. en_US
dc.description.sponsorship Oraintara, Soontorn en_US
dc.language.iso en en_US
dc.publisher Electrical Engineering en_US
dc.title Wavelet Based Array Comparative Genomic Hybridization And Mass Spectrometry Data Analysis en_US
dc.type Ph.D. en_US
dc.contributor.committeeChair Oraintara, Soontorn en_US
dc.degree.department Electrical Engineering en_US
dc.degree.discipline Electrical Engineering en_US
dc.degree.grantor University of Texas at Arlington en_US
dc.degree.level doctoral en_US
dc.degree.name Ph.D. en_US

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