Quick Hierarchical Biclustering on Microarray Gene Expression Data

Authors:
Liping Ji;Kenneth Wei-Liang Mock;Kian-Lee Tan
Affiliations:
National University of Singapore;National University of Singapore;National University of Singapore
Venue:
BIBE '06 Proceedings of the Sixth IEEE Symposium on BionInformatics and BioEngineering
Year:
2006

Citing 0
Cited 4

Maximal Subspace Coregulated Gene Clustering

IEEE Transactions on Knowledge and Data Engineering
Gene expression network discovery: a pattern based biclustering approach

Proceedings of the 2011 International Conference on Communication, Computing & Security
Biclustering of gene expression data based on related genes and conditions extraction

Pattern Recognition
CoBi: Pattern Based Co-Regulated Biclustering of Gene Expression Data

Pattern Recognition Letters

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Abstract

Mining biclusters that exhibit both consistent trends and trends with similar degrees of fluctuations is vital to bioinformatics research. However, existing biclustering methods are not very efficient and effective at mining such biclusters. Moreover, few inter-bicluster relationships are delivered to biologists. In this paper, we introduce a quick hierarchical biclustering algorithm (QHB) to efficiently mine biclusters with both consistent trends and trends with similar degrees of fluctuations. Our QHB produces not only biclusters but also a hierarchical graph of inter-bicluster relationships. We experimented with the Yeast dataset and compared QHB against an existing biclustering scheme, DBF. Our results show that QHB identifies biclusters with better quality. In addition, QHB shows the relationships among biclusters. Moreover, compared with DBF, QHB is much more efficient and offers users a progressive way of bicluster exploration.