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

  • Authors:

  • J. S. Yu;S. Ongarello;R. Fiedler;X. W. Chen;G. Toffolo;C. Cobelli;Z. Trajanoski

  • Affiliations:

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

  • Venue:
  • Bioinformatics
  • Year:
  • 2005

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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 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@tugraz.at