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

Si Ho Yoo, Chanho Park, Sung Bae Cho

Research output: Chapter in Book/Report/Conference proceedingConference 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 languageEnglish
Title of host publicationProceedings of the 2004 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB'04
Pages116-122
Number of pages7
Publication statusPublished - 2004 Dec 1
EventProceedings of the 2004 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB'04 - La Jolla, CA, United States
Duration: 2004 Oct 72004 Oct 8

Publication series

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

Other

OtherProceedings of the 2004 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB'04
CountryUnited States
CityLa Jolla, CA
Period04/10/704/10/8

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All Science Journal Classification (ASJC) codes

  • Engineering(all)

Cite this

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).