Bioinformatics Vol. 18 no. 8 2002
Pages 1073-1083
© 2002 Oxford University Press
Analysis of expression profile using fuzzy adaptive resonance theory
Department of Biotechnology, School of Engineering Nagoya University, Furo-cho, Chikusa-ku, Nagoya 464-8603, Japan
Received on September 3, 2001
; revised on January 1, 2002
; accepted on February 27, 2002
Motivation: It is well understood that the successful clustering of expression profiles give beneficial ideas to understand the functions of uncharacterized genes. In order to realize such a successful clustering, we investigate a clustering method based on adaptive resonance theory (ART) in this report.
Results: We apply Fuzzy ART as a clustering method for analyzing the time series expression data during sporulation of Saccharomyces cerevisiae. The clustering result by Fuzzy ART was compared with those by other clustering methods such as hierarchical clustering, k-means algorithm and self-organizing maps (SOMs). In terms of the mathematical validations, Fuzzy ART achieved the most reasonable clustering. We also verified the robustness of Fuzzy ART using noised data. Furthermore, we defined the correctness ratio of clustering, which is based on genes whose temporal expressions are characterized biologically. Using this definition, it was proved that the clustering ability of Fuzzy ART was superior to other clustering methods such as hierarchical clustering, k-means algorithm and SOMs. Finally, we validate the clustering results by Fuzzy ART in terms of biological functions and evidence.
Availability: The software is available at http//www.nubio.nagoya-u.ac.jp/proc/index.html
Contact: taizo{at}brs.kyushu-u.ac.jp
* To whom correspondence should be addressed. Present address: Laboratory for Applied Biological Regulation Technology, School of Bioresource and Bioenvironmental Science, Kyushu University, 6-10-1 Hakozaki, Higashi-ku, Fukuoka 812-8581, Japan.
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