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Bioinformatics Advance Access published online on March 22, 2005
Bioinformatics, doi:10.1093/bioinformatics/bti370
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© The Author (2005). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oupjournals.org
Received September 14, 2004
Revised March 1, 2005
Accepted March 1, 2005

Article

Ovarian cancer identification based on dimensionality reduction for high-throughput mass spectrometry data

J. S. Yu 1, S. Ongarello 2, R. Fiedler 3, X. W. Chen 4, G. Toffolo 5, C. Cobelli 5, and Z. Trajanoski 6*

1 School of Electronics Engineering and Computer Science, Peking University, China; Institute for Genomics and Bioinformatics Graz University of Technology, 8010 Graz, Austria
2 Department of Information Engineering, University of Padova, Italy; Institute for Genomics and Bioinformatics, Graz University of Technology, 8010 Graz, Austria
3 Institute for Genomics and Bioinformatics Graz University of Technology, 8010 Graz, Austria
4 Electrical Engineering and Computer Science Department, Information and Telecommunication Center, University of Kansas, USA
5 Department of Information Engineering, University of Padova, Italy
6 Institute for Genomics and Bioinformatics, Christian-Doppler Laboratory for Genomics and Bioinformatics Graz University of Technology, 8010 Graz, Austria

* To whom correspondence should be addressed.
Z. Trajanoski, E-mail: zlatko.trajanoski{at}tugraz.at


  Abstract

Motivation: High-throughput and high-resolution mass spectrometry instruments are increasingly used for disease classification and therapeutic guidance. However, the analysis of immense amount of data poses considerable challenges. We have therefore developed a novel method for dimensionality reduction and tested on a published ovarian high-resolution SELDI-TOF data set.

Results: We have developed a four-step strategy for data preprocessing based on: 1) binning, 2) Kolmogorov-Smirnov test, 3) restriction of coefficient of variation, and 4) wavelet analysis. Subsequently, Support Vector Machines (SVM) were used for classification. The developed method achieves an average sensitivity of 97.38% (sd=0.0125) and an average specificity of 93.30% (sd=0.0174) in 1,000 independent k-fold cross validations, where k = 2, ..., 10.

Availability: The software is available for academic and non-commercial institutions.


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