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Bioinformatics Advance Access published online on April 21, 2006
Bioinformatics, doi:10.1093/bioinformatics/btl156
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© The Author (2006). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org
Received March 13, 2006
Revised April 6, 2006
Accepted April 19, 2006

Applications note

Modified signal-to-noise: a new simple and practical gene filtering approach based on the concept of projective adaptive resonance theory (PART) filtering method

Hiro Takahashi 1 and Hiroyuki Honda 1 *

1 Department of Biotechnology, School of Engineering, Nagoya University, Furo-cho, Chikusa-ku, Nagoya 464-8603, Japan

* To whom correspondence should be addressed.
Hiroyuki Honda, E-mail: honda{at}nubio.nagoya-u.ac.jp


  Abstract

Summary: Considering the recent advances in and the benefits of DNA microarray technologies, many gene filtering approaches have been employed for the diagnosis and prognosis of diseases. In our previous study, we developed a new filtering method, namely, the projective adaptive resonance theory (PART) filtering method. This method was effective in subclass discrimination. In the PART algorithm, the genes with a low variance in gene expression in either class, not both classes, were selected as important genes for modeling. Based on this concept, we developed novel simple filtering methods such as modified signal-to-noise (S2N') in the present study. The discrimination model constructed using these methods showed higher accuracy with higher reproducibility as compared to many conventional filtering methods, including the t-test, S2N, NSC, and SAM. The reproducibility of prediction was evaluated based on the correlation between the sets of U-test p-values on randomly divided data sets. With respect to leukemia, lymphoma and breast cancer, the correlation was high; a difference of more than 0.13 was obtained by the constructed model by using less than 50 genes selected by S2N'. Improvement was higher in the smaller genes and such higher correlation was observed when t-test, NSC, and SAM were used. These results suggest that these modified methods, such as S2N', have high potential to function as new methods for marker gene selection in cancer diagnosis by using DNA microarray data.

Availability: Software is available upon request.

Associate Editor: Joaquin Dopazo
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