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

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

1 Citation (Scopus)

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 multi-resolution 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.

Original languageEnglish
Title of host publicationImage and Signal Processing - 4th International Conference, ICISP 2010, Proceedings
Pages593-600
Number of pages8
DOIs
Publication statusPublished - 2010
Event4th International Conference on Image and Signal Processing, ICISP 2010 - Trois-Rivieres, QC, Canada
Duration: 2010 Jun 302010 Jul 2

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6134 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other4th International Conference on Image and Signal Processing, ICISP 2010
CountryCanada
CityTrois-Rivieres, QC
Period10/6/3010/7/2

Keywords

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

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Fan, G., Wang, Z., Kim, S. B., & Temiyasathit, C. (2010). Classification of high-resolution NMR spectra based on complex wavelet domain feature selection and kernel-induced random forest. In Image and Signal Processing - 4th International Conference, ICISP 2010, Proceedings (pp. 593-600). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6134 LNCS). https://doi.org/10.1007/978-3-642-13681-8_69