An evolutionary approach for gene expression patterns

Huai Kuang Tsai*, Jinn-Moon Yang, Yuan Fang Tsai, Cheng Yan Kao

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

17 Scopus citations


This study presents an evolutionary algorithm, called a heterogeneous selection genetic algorithm (HeSGA), for analyzing the patterns of gene expression on microarray data. Microarray technologies have provided the means to monitor the expression levels of a large number of genes simultaneously. Gene clustering and gene ordering are important in analyzing a large body of microarray expression data. The proposed method simultaneously solves gene clustering and gene-ordering problems by integrating global and local search mechanisms. Clustering and ordering information is used to identify functionally related genes and to infer genetic networks from immense microarray expression data. HeSGA was tested on eight test microarray datasets, ranging in size from 147 to 6221 genes. The experimental clustering and visual results indicate that HeSGA not only ordered genes smoothly but also grouped genes with similar gene expressions. Visualized results and a new scoring function that references predefined functional categories were employed to confirm the biological interpretations of results yielded using HeSGA and other methods. These results indicate that HeSGA has potential in analyzing gene expression patterns.

Original languageEnglish
Pages (from-to)69-78
Number of pages10
JournalIEEE Transactions on Information Technology in Biomedicine
Issue number2
StatePublished - 1 Jun 2004


  • Clustering
  • Gene clustering
  • Gene expression
  • Gene ordering
  • Genetic algorithm (GA)
  • Heterogeneous pairing selection (HpS)
  • Microarray


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