Evolutionary fuzzy cluster analysis with Bayesian validation of gene expression profiles

Han Saem Park, Sung-Bae Cho

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

Clustering analysis of the gene expression profiles has been used for identifying the functions of unknown genes. Fuzzy clustering method, which is one category of clustering, assigns one sample to multiple clusters as their degrees of membership. It is more appropriate for analyzing gene expression profiles because genes usually belong to multiple functional families. However, general clustering methods have problems that they are sensitive to initialization and can be trapped into local optima. In this paper, we propose an evolutionary fuzzy clustering method with Bayesian validation which uses a genetic algorithm for fuzzy clustering process of gene expression profiles and Bayesian validation method for the fitness evaluation process. We have conducted in-depth experiments to verify the usefulness of the proposed method with well-known gene expression profiles of SRBCT and Saccharomyces.

Original languageEnglish
Pages (from-to)543-559
Number of pages17
JournalJournal of Intelligent and Fuzzy Systems
Volume18
Issue number6
Publication statusPublished - 2007 Dec 1

Fingerprint

Gene Expression Profile
Cluster analysis
Cluster Analysis
Gene expression
Fuzzy clustering
Fuzzy Clustering
Clustering Methods
Genes
Gene
Clustering Analysis
Initialization
Fitness
Assign
Genetic algorithms
Genetic Algorithm
Clustering
Verify
Unknown
Evaluation
Experiment

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Engineering(all)
  • Artificial Intelligence

Cite this

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Evolutionary fuzzy cluster analysis with Bayesian validation of gene expression profiles. / Park, Han Saem; Cho, Sung-Bae.

In: Journal of Intelligent and Fuzzy Systems, Vol. 18, No. 6, 01.12.2007, p. 543-559.

Research output: Contribution to journalArticle

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