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Bioinformatics Advance Access published online on February 1, 2006
Bioinformatics, doi:10.1093/bioinformatics/btl029
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© The Author (2006). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org
Received December 21, 2005
Revised January 26, 2006
Accepted January 26, 2006

Article

Structured polychotomous machine diagnosis of multiple cancer types using gene expression

Ja-Yong Koo 1 *, Insuk Sohn 1, Sujong Kim 2, and Jae Won Lee 1

1 Department of Statistics, Korea University, Seoul 136-701, Korea
2 Department of Biochemistry, College of Medicine, Hanyang University, Seoul 133-791, Korea; Current address: Skin Research Institute, AmorePacific R&D Center, Yongin 449-729, Korea

* To whom correspondence should be addressed.
Ja-Yong Koo, E-mail: jykoo{at}korea.ac.kr


  Abstract

Motivation: The problem of class prediction has received a tremendous amount of attention in the literature recently. In the context of DNA microarrays, where the task is to classify and predict the diagnostic category of a sample on the basis of its gene expression profile, a problem of particular importance is the diagnosis of cancer type based on microarray data. One method of classification which has been very successful in cancer diagnosis is the support vector machine. The latter has been shown (through simulations) to be superior in comparison to other methods, such as classical discriminant analysis, however, support vector machine suffers from the drawback that the solution is implicit and therefore is difficult to interpret. In order to remedy this difficulty, an analysis of variance decomposition using structured kernels is proposed and is referred to as the structured polychotomous machine. This technique utilizes Newton-Raphson to find estimates of coefficients followed by the Rao and Wald tests, respectively, for addition and deletion of import vectors.

Results: The proposed method is applied to microarray data and simulation data. The major breakthrough of our method is efficiency in that only a minimal number of genes that accurately predict the classes are selected. It has been verified that the selected genes serve as legitimate markers for cancer classification from a biological point of view.

Availability: All source codes used are available on request from the authors.

Associate Editor: Martin Bishop
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