WF-MSB: A weighted fuzzy-based biclustering method for gene expression data

Lien Chin Chen, Philip S. Yu, S. Tseng

Research output: Contribution to journalArticlepeer-review

19 Scopus citations

Abstract

Biclustering is an important analysis method on gene expression data for finding a subset of genes sharing compatible expression patterns. Although some biclustering algorithms have been proposed, few provided a query-driven approach for biologists to search the biclusters, which contain a certain gene of interest. In this paper, we proposed a generalised fuzzy-based approach, namely Weighted Fuzzy-based Maximum Similarity Biclustering (WF-MSB), for extracting a query-driven bicluster based on the user-defined reference gene. A fuzzy-based similarity measurement and condition weighting approach are used to extract significant biclusters in expression levels. Both of the most similar bicluster and the most dissimilar bicluster to the reference gene are discovered by WF-MSB. The proposed WF-MSB method was evaluated in comparison with MSBE on a real yeast microarray data and synthetic data sets. The experimental results show that WF-MSB can effectively find the biclusters with significant GO-based functional meanings.

Original languageEnglish
Pages (from-to)89-109
Number of pages21
JournalInternational Journal of Data Mining and Bioinformatics
Volume5
Issue number1
DOIs
StatePublished - 1 Feb 2011

Keywords

  • Biclustering
  • Data mining
  • Fuzzy set
  • Gene expression
  • Gene similarity measure

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