Bayesian validation of fuzzy clustering for analysis of yeast cell cycle data

Kyung Joong Kim, Si Ho Yoo, Sung Bae Cho

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Clustering for the analysis of the gene expression profiles has been used for identifying the functions of the genes and of unknown genes. Since the genes usually belong to multiple functional families, fuzzy clustering methods are more appropriate than the conventional hard clustering methods. However, it is still required to devise natural way to measure the quality of the cluster partitions that are obtained by fuzzy clustering. In this paper, a Bayesian validation method of selecting a fuzzy partition with the largest posterior probability given the dataset is proposed to evaluate the fuzzy partitions effectively. Analysis of yeast cell-cycle data follows to show the usefulness of the proposed method.

Original languageEnglish
Title of host publicationKnowledge-Based Intelligent Information and Engineering Systems - 9th International Conference, KES 2005, Proceedings
Pages777-784
Number of pages8
Publication statusPublished - 2005 Dec 1
Event9th International Conference on Knowledge-Based Intelligent Information and Engineering Systems, KES 2005 - Melbourne, Australia
Duration: 2005 Sep 142005 Sep 16

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3683 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other9th International Conference on Knowledge-Based Intelligent Information and Engineering Systems, KES 2005
CountryAustralia
CityMelbourne
Period05/9/1405/9/16

Fingerprint

Fuzzy clustering
Cell Cycle
Fuzzy Clustering
Yeast
Cluster Analysis
Fuzzy Partition
Genes
Yeasts
Cells
Gene
Clustering Methods
Gene Expression Profile
Posterior Probability
Gene expression
Bayes Theorem
Transcriptome
Partition
Clustering
Unknown
Evaluate

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Kim, K. J., Yoo, S. H., & Cho, S. B. (2005). Bayesian validation of fuzzy clustering for analysis of yeast cell cycle data. In Knowledge-Based Intelligent Information and Engineering Systems - 9th International Conference, KES 2005, Proceedings (pp. 777-784). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3683 LNAI).
Kim, Kyung Joong ; Yoo, Si Ho ; Cho, Sung Bae. / Bayesian validation of fuzzy clustering for analysis of yeast cell cycle data. Knowledge-Based Intelligent Information and Engineering Systems - 9th International Conference, KES 2005, Proceedings. 2005. pp. 777-784 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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Kim, KJ, Yoo, SH & Cho, SB 2005, Bayesian validation of fuzzy clustering for analysis of yeast cell cycle data. in Knowledge-Based Intelligent Information and Engineering Systems - 9th International Conference, KES 2005, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 3683 LNAI, pp. 777-784, 9th International Conference on Knowledge-Based Intelligent Information and Engineering Systems, KES 2005, Melbourne, Australia, 05/9/14.

Bayesian validation of fuzzy clustering for analysis of yeast cell cycle data. / Kim, Kyung Joong; Yoo, Si Ho; Cho, Sung Bae.

Knowledge-Based Intelligent Information and Engineering Systems - 9th International Conference, KES 2005, Proceedings. 2005. p. 777-784 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3683 LNAI).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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AB - Clustering for the analysis of the gene expression profiles has been used for identifying the functions of the genes and of unknown genes. Since the genes usually belong to multiple functional families, fuzzy clustering methods are more appropriate than the conventional hard clustering methods. However, it is still required to devise natural way to measure the quality of the cluster partitions that are obtained by fuzzy clustering. In this paper, a Bayesian validation method of selecting a fuzzy partition with the largest posterior probability given the dataset is proposed to evaluate the fuzzy partitions effectively. Analysis of yeast cell-cycle data follows to show the usefulness of the proposed method.

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Kim KJ, Yoo SH, Cho SB. Bayesian validation of fuzzy clustering for analysis of yeast cell cycle data. In Knowledge-Based Intelligent Information and Engineering Systems - 9th International Conference, KES 2005, Proceedings. 2005. p. 777-784. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).