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Biomarker Selection System, Employing an Iterative Peak Selection Method, for Identifying Biomarkers Related to Prostate Cancer

  • Panagiotis Bougioukos
  • Dionisis Cavouras
  • Antonis Daskalakis
  • Ioannis Kalatzis
  • Spiros Kostopoulos
  • Pantelis Georgiadis
  • George Nikiforidis
  • Anastasios Bezerianos
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4673)

Abstract

A biomarker selection system is proposed for identifying biomarkers related to prostate cancer. MS-spectra were obtained from the National Cancer Institute Clinical Proteomics Database. The system comprised two stages, a pre-processing stage, which is a sequence of MS-processing steps consisting of MS-spectrum smoothing, novel iterative peak selection, peak alignment, and a classification stage employing the PNN classifier. The proposed iterative peak selection method was based on first applying local thresholding, for determining the MS-spectrum noise level, and second applying an iterative global threshold estimation algorithm, for selecting peaks at different intensity ranges. At each global threshold, an optimum sub-set of these peaks was used to design the PNN classifier for highest performance, in discriminating normal cases from cases with prostate cancer, and thus indicate the best m/z values. Among these values, the information rich biomarkers 1160.8, 2082.2, 3595.9, 4275.3, 5817.3, 7653.2, that have been associated with the prostate gland, are proposed for further investigation.

Keywords

Mass-Spectrometry Biomarker Selection Classification 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Panagiotis Bougioukos
    • 1
  • Dionisis Cavouras
    • 2
  • Antonis Daskalakis
    • 1
  • Ioannis Kalatzis
    • 2
  • Spiros Kostopoulos
    • 1
  • Pantelis Georgiadis
    • 1
  • George Nikiforidis
    • 1
  • Anastasios Bezerianos
    • 1
  1. 1.Department of Medical Physics, School of Medicine, University of Patras, Rio, GR-26500Greece
  2. 2.Medical Signal and Image Processing Lab, Department of Medical Instrumentation Technology, Technological Education Institution of Athens, Ag. Spyridonos Street, Aigaleo, 122 10, AthensGreece

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