Classification of High-Resolution NMR Spectra Based on Complex Wavelet Domain Feature Selection and Kernel-Induced Random Forest

  • Guangzhe Fan
  • Zhou Wang
  • Seoung Bum Kim
  • Chivalai Temiyasathit
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6134)

Abstract

High-resolution nuclear magnetic resonance (NMR) spectra contain important biomarkers that have potentials for early diagnosis of disease and subsequent monitoring of its progression. Traditional features extraction and analysis methods have been carried out in the original frequency spectrum domain. In this study, we conduct feature selection based on a complex wavelet transform by making use of its energy shift-insensitive property in a multiresolution signal decomposition. A false discovery rate based multiple testing procedure is employed to identify important metabolite features. Furthermore, a novel kernel-induced random forest algorithm is used for the classification of NMR spectra based on the selected features. Our experiments with real NMR spectra showed that the proposed method leads to significant reduction in misclassification rate.

Keywords

High-resolution NMR spectrum Metabolomics Classification tree Random forest Complex wavelet transforms False discovery rate Kernel 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Guangzhe Fan
    • 1
  • Zhou Wang
    • 2
  • Seoung Bum Kim
    • 3
  • Chivalai Temiyasathit
    • 4
  1. 1.Dept. of Statistics & Acturial ScienceUniversity of WaterlooWaterlooCanada
  2. 2.Dept. of Electrical & Computer EngineeringUniversity of WaterlooWaterlooCanada
  3. 3.Dept. Industrial Systems & Information EngineeringKorea UniversitySeoulKorea
  4. 4.International CollegeKing Mongkut’s Inst. of Technology LadkrabangBangkokThailand

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