Heon Gyu Lee , Keun Ho Ryu , Doo Young Joung

The KIPS Transactions:PartD, Vol. 14, No. 1, pp. 9-20, Feb. 2007
10.3745/KIPSTD.2007.14.1.9,   PDF Download:

Abstract

Groups of genes control the functioning of a cell by complex interactions. Such interactions of gene groups are called Gene Regulatory Networks(GRNs). Two previous data mining approaches, clustering and classification, have been used to analyze gene expression data. Though these mining tools are useful for determining membership of genes by homology, they don't identify the regulatory relationships among genes found in the same class of molecular actions. Furthermore, we need to understand the mechanism of how genes relate and how they regulate one another. In order to detect regulatory relationships among genes from time-series Microarray data, we propose a novel approach using frequent pattern mining and chain rules. In this approach, we propose a method for transforming gene expression data to make suitable for frequent pattern mining, and gene expression patterns are detected by applying the FP-growth algorithm. Next, we construct a gene regulatory network from frequent gene patterns using chain rules. Finally, we validate our proposed method through our experimental results, which are consistent with published results.

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