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Support Vector Machine Approach to Separate Control and Breast Cancer Serum Samples

Author

Listed:
  • Pham Thang V

    (Vrije Universiteit Medical Center)

  • van de Wiel Mark A

    (Vrije Universiteit Medical Center)

  • Jimenez Connie R

    (Vrije Universiteit Medical Center)

Abstract

The paper presents two analyzes of the MALDI-TOF mass spectrometry dataset. Both analyzes use the support vector machine as a tool to build a prediction model. The first analysis which is our contribution to the competition uses the given spectra data without further processing. In the second analysis, we employed an additional preprocessing step consisting of peak detection, peak alignment and feature selection based on statistical tests. The experimental results suggest that the preprocessing step with feature selection improves prediction accuracy.

Suggested Citation

  • Pham Thang V & van de Wiel Mark A & Jimenez Connie R, 2008. "Support Vector Machine Approach to Separate Control and Breast Cancer Serum Samples," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 7(2), pages 1-11, February.
    • Handle: RePEc:bpj:sagmbi:v:7:y:2008:i:2:n:11
    DOI: 10.2202/1544-6115.1355
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    Cited by:

    1. Hand David J, 2008. "Breast Cancer Diagnosis from Proteomic Mass Spectrometry Data: A Comparative Evaluation," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 7(2), pages 1-23, December.

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