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A New Wavelet-Based Approach for Mass Spectrometry Data Classification

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New Frontiers of Biostatistics and Bioinformatics

Part of the book series: ICSA Book Series in Statistics ((ICSABSS))

Abstract

Proteomic patterns can help the diagnosis of the underlying pathological state of an organ such as the ovary, the lung, and the breast, to name a few. An accurate classification of mass spectrometry is a crucial point to establish a reliable diagnosis and decision process regarding the type of cancer. A statistical methodology for classifying mass spectrometry data is proposed. An overview of wavelets, principal component analysis-T 2 statistic, and support vector machines is given. The study is performed on low-mass SELDI spectra derived from patients with breast cancer and from normal controls. There are 156 samples where control (normal) patients contribute with 57 samples and 99 samples are cancer. A hyperparameter optimization is conducted to select a support vector machine classification model based on grid search. The performance was evaluated with a k-fold cross validation technique and Monte-Carlo simulation with 100 replications. The average accuracy is 100% with standard error equals to 0. The averages of the sensitivity and specificity are both equal to 100%, as well as the area under the curve. The excellent performance of our proposed method is mainly due to the statistical modeling and the feature extraction procedure proposed.

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Author information

Authors and Affiliations

  1. Department of Mathematics and Statistics, University of West Florida, Pensacola, FL, USA

    Achraf Cohen

  2. National School of Applied Sciences-Tangier, ENSAT, Abdelmalek Essaadi University, Tangier, Morocco

    Chaimaa Messaoudi & Hassan Badir

Authors
  1. Achraf Cohen
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  2. Chaimaa Messaoudi
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  3. Hassan Badir
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Corresponding author

Correspondence to Achraf Cohen .

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Editors and Affiliations

  1. Department of Mathematics and Statistics, Georgia State University, Atlanta, GA, USA

    Yichuan Zhao

  2. Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Ding-Geng Chen

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Cohen, A., Messaoudi, C., Badir, H. (2018). A New Wavelet-Based Approach for Mass Spectrometry Data Classification. In: Zhao, Y., Chen, DG. (eds) New Frontiers of Biostatistics and Bioinformatics. ICSA Book Series in Statistics. Springer, Cham. https://doi.org/10.1007/978-3-319-99389-8_8

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