Bi-k-bi clustering: mining large scale gene expression data using two-level biclustering
International Journal of Data Mining and Bioinformatics
Inderscience Enterprises Ltd.
701 - 721
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Please cite this item using this persistent URLhttp://hdl.handle.net/11693/22099
Due to the increase in gene expression data sets in recent years, various data mining techniques have been proposed for mining gene expression profiles. However, most of these methods target single gene expression data sets and cannot handle all the available gene expression data in public databases in reasonable amount of time and space. In this paper, we propose a novel framework, bi-k-bi clustering, for finding association rules of gene pairs that can easily operate on large scale and multiple heterogeneous data sets. We applied our proposed framework on the available NCBI GEO Homo sapiens data sets. Our results show consistency and relatedness with the available literature and also provides novel associations. Copyright © 2010 Inderscience Enterprises Ltd.
Association pattern discovery
Gene expression analysis
Spearman rank correlation