A wavelet-based data pre-processing analysis approach in mass spectrometry

Xiaoli LI, Jin LI, Xin YAO

Research output: Journal PublicationsJournal Article (refereed)peer-review

19 Citations (Scopus)


Recently, mass spectrometry analysis has a become an effective and rapid approach in detecting early-stage cancer. To identify proteomic patterns in serum to discriminate cancer patients from normal individuals, machine-learning methods, such as feature selection and classification, have already been involved in the analysis of mass spectrometry (MS) data with some success. However, the performance of existing machine learning methods for MS data analysis still needs improving. The study in this paper proposes a wavelet-based pre-processing approach to MS data analysis. The approach applies wavelet-based transforms to MS data with the aim of de-noising the data that are potentially contaminated in acquisition. The effects of the selection of wavelet function and decomposition level on the de-noising performance have also been investigated in this study. Our comparative experimental results demonstrate that the proposed de-noising pre-processing approach has potentials to remove possible noise embedded in MS data, which can lead to improved performance for existing machine learning methods in cancer detection. © 2006 Elsevier Ltd. All rights reserved.
Original languageEnglish
Pages (from-to)509-516
Number of pages8
JournalComputers in Biology and Medicine
Issue number4
Early online date19 Sept 2006
Publication statusPublished - Apr 2007
Externally publishedYes

Bibliographical note

The authors are grateful to Mr. T. Qiu for testing the program. The constructive comments from reviewers are appreciated. The work is supported by Cercia, The Centre of Excellence for Research in Computational Intelligence and Applications, in the School of Computer Science at the University of Birmingham, UK.


  • Cancer detection
  • De-noising
  • Linear discriminate analysis
  • Mass spectrometry
  • Principal component analysis
  • Probabilistic classification
  • Wavelet transforms


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