DocumentCode :
819837
Title :
Spectral Pattern Comparison Methods for Cancer Classification Based on Microarray Gene Expression Data
Author :
Pham, Tuan D. ; Beck, Dominik ; Yan, Hong
Author_Institution :
Sch. of Inf. Technol., James Cook Univ. of North Queensland, Townsville, Qld.
Volume :
53
Issue :
11
fYear :
2006
Firstpage :
2425
Lastpage :
2430
Abstract :
We present, in this paper, two spectral pattern comparison methods for cancer classification using microarray gene expression data. The proposed methods are different from other current classifiers in the ways features are selected and pattern similarities measured. In addition, these spectral methods do not require any data preprocessing which is necessary for many other classification techniques. Experimental results using three popular microarray data sets demonstrate the robustness and effectiveness of the spectral pattern classifiers
Keywords :
cancer; feature extraction; genetics; pattern recognition; cancer classification; feature selection; microarray gene expression data; microarrays; spectral distortions; spectral pattern comparison methods; vector quantization; Bayesian methods; Cancer; DNA; Distortion measurement; Fluorescence; Gene expression; Pharmaceutical technology; Predictive models; Robustness; Support vector machines; Classification; feature selection; microarrays; spectral distortions; vector quantization;
fLanguage :
English
Journal_Title :
Circuits and Systems I: Regular Papers, IEEE Transactions on
Publisher :
ieee
ISSN :
1549-8328
Type :
jour
DOI :
10.1109/TCSI.2006.884407
Filename :
4012359
Link To Document :
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