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Received: 2008-05-15

Revision Accepted: 2008-07-30

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Journal of Zhejiang University SCIENCE B 2008 Vol.9 No.11 P.863~870

http://doi.org/10.1631/jzus.B0820163


A data-mining approach to biomarker identification from protein profiles using discrete stationary wavelet transform


Author(s):  Hussain MONTAZERY-KORDY, Mohammad Hossein MIRAN-BAYGI, Mohammad Hassan MORADI

Affiliation(s):  Department of Electrical and Computer Engineering, Tarbiat Modares University, P.O. Box 14115-111, Tehran, Iran; more

Corresponding email(s):   Miranbmh@modares.ac.ir

Key Words:  Proteomics, Discrete stationary wavelet transform, Data mining, Feature selection, Biomarker, Cancer classification


Hussain MONTAZERY-KORDY, Mohammad Hossein MIRAN-BAYGI, Mohammad Hassan MORADI. A data-mining approach to biomarker identification from protein profiles using discrete stationary wavelet transform[J]. Journal of Zhejiang University Science B, 2008, 9(11): 863~870.

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author="Hussain MONTAZERY-KORDY, Mohammad Hossein MIRAN-BAYGI, Mohammad Hassan MORADI",
journal="Journal of Zhejiang University Science B",
volume="9",
number="11",
pages="863~870",
year="2008",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.B0820163"
}

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%T A data-mining approach to biomarker identification from protein profiles using discrete stationary wavelet transform
%A Hussain MONTAZERY-KORDY
%A Mohammad Hossein MIRAN-BAYGI
%A Mohammad Hassan MORADI
%J Journal of Zhejiang University SCIENCE B
%V 9
%N 11
%P 863~870
%@ 1673-1581
%D 2008
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.B0820163

TY - JOUR
T1 - A data-mining approach to biomarker identification from protein profiles using discrete stationary wavelet transform
A1 - Hussain MONTAZERY-KORDY
A1 - Mohammad Hossein MIRAN-BAYGI
A1 - Mohammad Hassan MORADI
J0 - Journal of Zhejiang University Science B
VL - 9
IS - 11
SP - 863
EP - 870
%@ 1673-1581
Y1 - 2008
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.B0820163


Abstract: 
Objective: To develop a new bioinformatic tool based on a data-mining approach for extraction of the most informative proteins that could be used to find the potential biomarkers for the detection of cancer. Methods: Two independent datasets from serum samples of 253 ovarian cancer and 167 breast cancer patients were used. The samples were examined by surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS). The datasets were used to extract the informative proteins using a data-mining method in the discrete stationary wavelet transform domain. As a dimensionality reduction procedure, the hard thresholding method was applied to reduce the number of wavelet coefficients. Also, a distance measure was used to select the most discriminative coefficients. To find the potential biomarkers using the selected wavelet coefficients, we applied the inverse discrete stationary wavelet transform combined with a two-sided t-test. Results: From the ovarian cancer dataset, a set of five proteins were detected as potential biomarkers that could be used to identify the cancer patients from the healthy cases with accuracy, sensitivity, and specificity of 100%. Also, from the breast cancer dataset, a set of eight proteins were found as the potential biomarkers that could separate the healthy cases from the cancer patients with accuracy of 98.26%, sensitivity of 100%, and specificity of 95.6%. Conclusion: The results have shown that the new bioinformatic tool can be used in combination with the high-throughput proteomic data such as SELDI-TOF MS to find the potential biomarkers with high discriminative power.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

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