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. 2009 Nov 1:2009:254-259.
doi: 10.1109/BIBMW.2009.5332104.

Mining Association Rules among Gene Functions in Clusters of Similar Gene Expression Maps

Li An  1 , Zoran Obradovic  2 , Desmond Smith  3 , Olivier Bodenreider  4 , Vasileios Megalooikonomou
Affiliations

Mining Association Rules among Gene Functions in Clusters of Similar Gene Expression Maps

Li An et al. IEEE Int Conf Bioinform Biomed Workshops. .

Abstract

Association rules mining methods have been recently applied to gene expression data analysis to reveal relationships between genes and different conditions and features. However, not much effort has focused on detecting the relation between gene expression maps and related gene functions. Here we describe such an approach to mine association rules among gene functions in clusters of similar gene expression maps on mouse brain. The experimental results show that the detected association rules make sense biologically. By inspecting the obtained clusters and the genes having the gene functions of frequent itemsets, interesting clues were discovered that provide valuable insight to biological scientists. Moreover, discovered association rules can be potentially used to predict gene functions based on similarity of gene expression maps.

Keywords: association rules mining; clustering; gene expression maps; gene functions; voxelation.

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Figures

Fig. 1
Fig. 1
Voxels of the coronal slice
Fig. 2
Fig. 2
The 38 significant clusters found with respect to Cellular Component
Fig. 3
Fig. 3
Gene expression maps and curves of the 14 genes with the itemset1 of gene functions
Fig. 4
Fig. 4
Gene expression maps and curves of the 2 genes with the itemset3 of gene functions
Fig. 4
Fig. 4
Gene expression maps and curves of the 2 genes with the itemset3 of gene functions
Fig. 5
Fig. 5
Genes in the 36th cluster of Cellular Component
See this image and copyright information in PMC

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