A Wavelet-based Data Pre-processing Analysis Approach in Mass Spectrometry

Xiaoli Li, Jin Li, Xin Yao

Research output: Contribution to journalArticle

17 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.
Original languageEnglish
Pages (from-to)509-516
Number of pages8
JournalComputers in Biology and Medicine
Issue number4
Publication statusPublished - 1 Apr 2007


Dive into the research topics of 'A Wavelet-based Data Pre-processing Analysis Approach in Mass Spectrometry'. Together they form a unique fingerprint.

Cite this