Fast cancer classification based on mass spectrometry analysis in robust stationary wavelet domain

Phuong Pham, Li Yu, Minh Nguyen, Nha Nguyen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Mass spectrometry (MS) is a technology recently used for high dimensionality detection of proteins in proteomics. However, due to the high resolution and noise of MS data (MALDI-TOF), almost existing MS analysis algorithms are not robust with noise and run slowly. Developing new ones is necessary to analyze such data. In this paper, we propose a novel feature extraction method considering the inherent noise of mass spectra. The proposed method combines stationary wavelet transformation (SWT) and bivariate shrinkage estimator for MS feature extraction and denoising. Then, statistical feature testing is applied to denoised wavelet coefficients to select significant features used for biomarker identification. To evaluate the effectiveness of proposed method, a double cross-validation support vector machine classifier, which has high generalizability, and a fast Modest AdaBoost classifier, which improves significantly experimental runtime, are applied for cancer classification based on selected features by proposed method. Several experiments are carried out to evaluate the performance of our proposed methods. The results show that our proposed method can be an effective tool for analyzing MS data.

Original languageEnglish (US)
Title of host publicationIT Convergence and Services, ITCS 2011 and IRoA 2011
Pages189-199
Number of pages11
DOIs
StatePublished - Jan 1 2012
Externally publishedYes
Event3rd International Conference on Information Technology Convergence and Services, ITCS 2011 and 2011 FTRA International Conference on Intelligent Robotics, Automations, Telecommunication Facilities, and Applications, IRoA 2011 - Gwangju, Korea, Republic of
Duration: Oct 20 2011Oct 22 2011

Publication series

NameLecture Notes in Electrical Engineering
Volume107 LNEE
ISSN (Print)1876-1100
ISSN (Electronic)1876-1119

Other

Other3rd International Conference on Information Technology Convergence and Services, ITCS 2011 and 2011 FTRA International Conference on Intelligent Robotics, Automations, Telecommunication Facilities, and Applications, IRoA 2011
CountryKorea, Republic of
CityGwangju
Period10/20/1110/22/11

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Keywords

  • Bivariate shrinkage
  • Boosting
  • Feature extraction
  • Mass spectrometry
  • SVM
  • SWT

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering

Cite this

Pham, P., Yu, L., Nguyen, M., & Nguyen, N. (2012). Fast cancer classification based on mass spectrometry analysis in robust stationary wavelet domain. In IT Convergence and Services, ITCS 2011 and IRoA 2011 (pp. 189-199). (Lecture Notes in Electrical Engineering; Vol. 107 LNEE). https://doi.org/10.1007/978-94-007-2598-0_21