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

Authors: Lien-Chin Chen, Philip S. Yu, Vincent S. Tseng

Addresses: Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan 701, Taiwan. ' Department of Computer Science, University of Illinois at Chicago, Chicago, IL 60607, USA. ' Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan 701, Taiwan; and Institute of Medical Informatics, National Cheng Kung University, Tainan 701, Taiwan

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.

Keywords: biclustering; gene expression data; data mining; fuzzy sets; gene similarity measures; condition weighting; yeast microarray; biclusters.

DOI: 10.1504/IJDMB.2011.038579

International Journal of Data Mining and Bioinformatics, 2011 Vol.5 No.1, pp.89 - 109

Received: 08 Jul 2009
Accepted: 23 Oct 2009
Published online: 24 Jan 2015 *

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