Analyzing fuzzy partitions of Saccharomyces cerevisiae cell-cycle gene expression data by Bayesian validation method

Si Ho Yoo; Chanho Park; Sung Bae Cho

Analyzing fuzzy partitions of Saccharomyces cerevisiae cell-cycle gene expression data by Bayesian validation method

Si Ho Yoo, Chanho Park, Sung Bae Cho

College of Computing

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

4 Citations (Scopus)

Abstract

Clustering of gene expression profiles has been used for gene function identification. Since the genes usually belong to multiple functional families, fuzzy clustering methods are appropriate. However, a natural way to measure the quality of the fuzzy cluster partitions is still required. In this paper, a Bayesian validation method for fuzzy partition selection with the largest posterior probability given the dataset is proposed. This method is compared to four representative fuzzy cluster validity measures using fuzzy c-means algorithm on four well-known datasets in terms of the number of clusters predicted in the data. An analysis of Saccharomyces cerevisiae cell cycle gene expression data follows to show the usefulness of the proposed method.

Original language	English
Title of host publication	Proceedings of the 2004 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB'04
Pages	116-122
Number of pages	7
Publication status	Published - 2004
Event	Proceedings of the 2004 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB'04 - La Jolla, CA, United States Duration: 2004 Oct 7 → 2004 Oct 8

Publication series

Name	Proceedings of the 2004 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB'04

Other

Other	Proceedings of the 2004 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB'04
Country/Territory	United States
City	La Jolla, CA
Period	04/10/7 → 04/10/8

All Science Journal Classification (ASJC) codes

Engineering(all)

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Yoo, S. H., Park, C., & Cho, S. B. (2004). Analyzing fuzzy partitions of Saccharomyces cerevisiae cell-cycle gene expression data by Bayesian validation method. In Proceedings of the 2004 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB'04 (pp. 116-122). (Proceedings of the 2004 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB'04).

Yoo, Si Ho ; Park, Chanho ; Cho, Sung Bae. / Analyzing fuzzy partitions of Saccharomyces cerevisiae cell-cycle gene expression data by Bayesian validation method. Proceedings of the 2004 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB'04. 2004. pp. 116-122 (Proceedings of the 2004 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB'04).

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title = "Analyzing fuzzy partitions of Saccharomyces cerevisiae cell-cycle gene expression data by Bayesian validation method",

abstract = "Clustering of gene expression profiles has been used for gene function identification. Since the genes usually belong to multiple functional families, fuzzy clustering methods are appropriate. However, a natural way to measure the quality of the fuzzy cluster partitions is still required. In this paper, a Bayesian validation method for fuzzy partition selection with the largest posterior probability given the dataset is proposed. This method is compared to four representative fuzzy cluster validity measures using fuzzy c-means algorithm on four well-known datasets in terms of the number of clusters predicted in the data. An analysis of Saccharomyces cerevisiae cell cycle gene expression data follows to show the usefulness of the proposed method.",

author = "Yoo, {Si Ho} and Chanho Park and Cho, {Sung Bae}",

year = "2004",

language = "English",

isbn = "0780387287",

series = "Proceedings of the 2004 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB'04",

pages = "116--122",

booktitle = "Proceedings of the 2004 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB'04",

note = "Proceedings of the 2004 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB'04 ; Conference date: 07-10-2004 Through 08-10-2004",

}

Yoo, SH, Park, C & Cho, SB 2004, Analyzing fuzzy partitions of Saccharomyces cerevisiae cell-cycle gene expression data by Bayesian validation method. in Proceedings of the 2004 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB'04. Proceedings of the 2004 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB'04, pp. 116-122, Proceedings of the 2004 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB'04, La Jolla, CA, United States, 04/10/7.

Analyzing fuzzy partitions of Saccharomyces cerevisiae cell-cycle gene expression data by Bayesian validation method. / Yoo, Si Ho; Park, Chanho; Cho, Sung Bae.

Proceedings of the 2004 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB'04. 2004. p. 116-122 (Proceedings of the 2004 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB'04).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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PY - 2004

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N2 - Clustering of gene expression profiles has been used for gene function identification. Since the genes usually belong to multiple functional families, fuzzy clustering methods are appropriate. However, a natural way to measure the quality of the fuzzy cluster partitions is still required. In this paper, a Bayesian validation method for fuzzy partition selection with the largest posterior probability given the dataset is proposed. This method is compared to four representative fuzzy cluster validity measures using fuzzy c-means algorithm on four well-known datasets in terms of the number of clusters predicted in the data. An analysis of Saccharomyces cerevisiae cell cycle gene expression data follows to show the usefulness of the proposed method.

AB - Clustering of gene expression profiles has been used for gene function identification. Since the genes usually belong to multiple functional families, fuzzy clustering methods are appropriate. However, a natural way to measure the quality of the fuzzy cluster partitions is still required. In this paper, a Bayesian validation method for fuzzy partition selection with the largest posterior probability given the dataset is proposed. This method is compared to four representative fuzzy cluster validity measures using fuzzy c-means algorithm on four well-known datasets in terms of the number of clusters predicted in the data. An analysis of Saccharomyces cerevisiae cell cycle gene expression data follows to show the usefulness of the proposed method.

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T3 - Proceedings of the 2004 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB'04

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BT - Proceedings of the 2004 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB'04

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Yoo SH, Park C, Cho SB. Analyzing fuzzy partitions of Saccharomyces cerevisiae cell-cycle gene expression data by Bayesian validation method. In Proceedings of the 2004 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB'04. 2004. p. 116-122. (Proceedings of the 2004 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB'04).

Analyzing fuzzy partitions of Saccharomyces cerevisiae cell-cycle gene expression data by Bayesian validation method

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