Bioinformatics Advance Access originally published online on March 22, 2005
Bioinformatics 2005 21(10):2200-2209; doi:10.1093/bioinformatics/bti370
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Ovarian cancer identification based on dimensionality reduction for high-throughput mass spectrometry data
1School of Electronics Engineering and Computer Science, Peking University China
2Institute for Genomics and Bioinformatics, Graz University of Technology 8010 Graz, Austria
3Department of Information Engineering, University of Padova Italy
4Electrical Engineering and Computer Science Department, Information and Telecommunication Center, University of Kansas USA
5Institute for Genomics and Bioinformatics, Christian-Doppler Laboratory for Genomics and Bioinformatics, Graz University of Technology 8010 Graz, Austria
*To whom correspondence should be addressed.
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 dataset.
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 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 1000 independent k-fold cross-validations, where k = 2, ..., 10.
Availability: The software is available for academic and non-commercial institutions.
Contact: zlatko.trajanoski{at}tugraz.at
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