Advertisement

Analysis of SELDI-TOF-MS Using ε-Support Vector Regression for Ovarian Cancer Identification

  • Isye Arieshanti
  • Yudhi Purwananto
Conference paper
  • 114 Downloads
Part of the IFMBE Proceedings book series (IFMBE, volume 43)

Abstract

The analysis of protein expression profile using SELDI-TOF-MS can assist early detection of ovarian cancer. The chance to save patient’s life is greater when ovarian cancer is detected at an early stage. However, the analysis of protein expression profile is challenging because it has very high dimensional features and noisy characteristic. In order to tackle these limitations, the ε-Support Vector Regression model to identify ovarian cancer is proposed. We can show that the performance of the model to discriminate the protein expression profile with cancer disease from the normal ones can reach accuracy 99%, specificity 99% and sensitivity 100%. This result shows that the model is promising for SELDI-TOFMS analysis in Ovarian Cancer identification.

Keywords

SELDI-TOF-MS Ovarian Cancer Support Vector Regression 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Isye Arieshanti
    • 1
  • Yudhi Purwananto
    • 1
  1. 1.Institut Teknologi Sepuluh NopemberSurabayaIndonesia

Personalised recommendations