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Bioinformatics Advance Access published online on May 18, 2006
Bioinformatics, doi:10.1093/bioinformatics/btl190
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© The Author (2006). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org
Received November 21, 2005
Revised April 27, 2006
Accepted May 11, 2006

Article

Independent component analysis based penalized discriminant method for tumor classification using gene expression data

De-Shuang Huang 1 * and Chun-Hou Zheng 2

1 Intelligent Computing Lab, Institute of Intelligent Machines, Chinese Academy of Sciences, P.O.Box 1130, Hefei, Anhui 230031, China
2 Intelligent Computing Lab, Institute of Intelligent Machines, Chinese Academy of Sciences, P.O.Box 1130, Hefei, Anhui 230031, China; Department of Automation, University of Science and Technology of China

* To whom correspondence should be addressed.
De-Shuang Huang, E-mail: dshuang{at}iim.ac.cn


  Abstract

Motivation: Microarrays are capable of determining the expression levels of thousands of genes simultaneously. One important application of gene expression data is classification of samples into categories. In combination with classification methods, this technology can be useful to support clinical management decisions for individual patients, e.g., in oncology. Standard statistic methodologies in classification or prediction do not work well when the number of variables p (genes) is far to exceed the number of samples n. So, modification of existing statistical methodologies or development of new methodologies is needed for the analysis of microarray data.

Results: This paper proposes a new method for tumor classification using gene expression data. In this method, we first employ independent component analysis (ICA) to model the gene expression data, then apply optimal scoring algorithm to classify them. Further speaking, this approach can first make full use of the high-order statistical information contained in the gene expression data. Second, this approach also employs regularized regression models to handle the situation of large numbers of correlated predictor variables. Finally, the predictive models are developed for classifying tumors based on the entire gene expression profile. To show the validity of the proposed method, we apply it to classify four DNA microarray data sets involving various human normal and tumor tissue samples. The experimental results show that the method is efficient and feasible.

Availability: Matlab scripts are available on request.

Associate Editor: Martin Bishop
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