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This is the preliminary program for the 2007 Joint Statistical Meetings in Salt Lake City, Utah.

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Activity Number: 417
Type: Contributed
Date/Time: Wednesday, August 1, 2007 : 10:30 AM to 12:20 PM
Sponsor: Biometrics Section
Abstract - #308750
Title: A Novel Wavelet Approach for Denoising Mass Spectrometry Data
Author(s): Jaesik Jeong*+
Companies: Texas A&M University
Address: 1100 Hensel Dr Apt U1K, College Station, TX, 77840,
Keywords: Maximal overlap discrete wavelet transform ; mass spectromety ; heteroscedastic error model ; SELDI TOF M/S
Abstract:

In recent years, there has been an increased interest in using protein mass spectroscopy to detect discriminating molecular markers. One of popular analyses in proteomics is based on mass spectrometry data. When we analyze this kind of data, it is necessary to preprocess data. With poorly preprocessed data we have minuscule chance to find biomarkers. Wavelet denoising technique becomes one of standard approach to preprocessing procedure. However, existing wavelet denoising technique does not reflect the existence of heterogeneity error in mass spectrometry data. In this paper, we propose a novel wavelet denoising to deal with the heterogenity.


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Revised September, 2007