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Mass spectrometry cancer data classification using wavelets and genetic algorithm

Nguyen, Thanh, Nahavandi, Saeid, Creighton, Douglas and Khosravi, Abbas 2015, Mass spectrometry cancer data classification using wavelets and genetic algorithm, FEBS letters, vol. 589, no. 24 part b, pp. 3879-3886, doi: 10.1016/j.febslet.2015.11.019.

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Title Mass spectrometry cancer data classification using wavelets and genetic algorithm
Author(s) Nguyen, Thanh
Nahavandi, Saeid
Creighton, Douglas
Khosravi, Abbas
Journal name FEBS letters
Volume number 589
Issue number 24 part b
Start page 3879
End page 3886
Total pages 8
Publisher Elseiver
Place of publication Amsterdam, The Netherlands
Publication date 2015-12-21
ISSN 1873-3468
Keyword(s) Cancer classification
Feature extraction
Genetic algorithm
Mass spectrometry data
Wavelet transformation
Summary This paper introduces a hybrid feature extraction method applied to mass spectrometry (MS) data for cancer classification. Haar wavelets are employed to transform MS data into orthogonal wavelet coefficients. The most prominent discriminant wavelets are then selected by genetic algorithm (GA) to form feature sets. The combination of wavelets and GA yields highly distinct feature sets that serve as inputs to classification algorithms. Experimental results show the robustness and significant dominance of the wavelet-GA against competitive methods. The proposed method therefore can be applied to cancer classification models that are useful as real clinical decision support systems for medical practitioners.
Language eng
DOI 10.1016/j.febslet.2015.11.019
Field of Research 080108 Neural, Evolutionary and Fuzzy Computation
080109 Pattern Recognition and Data Mining
0601 Biochemistry And Cell Biology
0304 Medicinal And Biomolecular Chemistry
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2015, Elsevier
Persistent URL http://hdl.handle.net/10536/DRO/DU:30080140

Document type: Journal Article
Collection: Centre for Intelligent Systems Research
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Created: Tue, 08 Dec 2015, 12:18:54 EST

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