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

Author information

1
Data Engineering Laboratory, Dept. of Computer and Information Sciences, Temple University, PA, USA.
2
Center for Information Science and Technology, Temple University, PA, USA.
3
Dept. of Molecular and Medical Pharmacology, David Geffen School of Medicine, UCLA, CA, USA.
4
The Lister Hill National Center for Biomedical Communications, U.S. National Library of Medicine, Washington D.C., USA.

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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