Common Characteristics and Noise Filtering and its Application in a Proteomic Pattern Recognition System for Cancer Detection

Lit Hsin Loo, John Quinn, Hayley Cordingley, Samuel Roberts, Leonid Hrebien, Moshe Kam

Research output: Contribution to journalConference articlepeer-review

Abstract

High-throughput mass spectrometry technologies, such as surface enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-ToF-MS), generate large sets of complex data. The high dimensionality of these datasets poses analytical and computational challenges to the task of spectrum classification. In this paper, we describe a common characteristics and noise filter, which hones in on spectrum subsets with high discriminatory power. The filter is incorporated in a proteomic pattern recognition system. Our method is demonstrated on a set of 322 SELDI-ToF mass spectra of serum samples from prostate cancer patients and a control group. We show that our system can extract the discriminatory subsets from these spectra, and improve classification accuracy and computational speed compared to existing techniques.

Original languageEnglish (US)
Pages (from-to)2897-2900
Number of pages4
JournalAnnual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
Volume3
StatePublished - 2003
Externally publishedYes
EventA New Beginning for Human Health: Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society - Cancun, Mexico
Duration: Sep 17 2003Sep 21 2003

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

Keywords

  • Common characteristics and noise filtering
  • Genetic algorithm
  • Mass spectrum
  • Normalization
  • Proteomics
  • SELDI

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