Abstract—Association rules, used widely in the area of market basket analysis, can be applied to the analysis of expression data as well. Association rules can reveal biologically relevant associations between different genes or between environmental effects and gene expression. An association rule has the form LHS→RHS, where LHS and RHS are disjoint sets of items, the RHS set being likely to occur whenever the LHS set occurs. Items in gene expression data can include genes that are highly expressed or repressed, as well as relevant facts describing the cellular environment of the genes (e.g. the diagnosis of a tumor sample from which a profile was obtained). In this paper, association rule mining techniques that have been recently developed and used for genomic data analysis have been reviewed and discussed.
Index Terms—Association Rule Mining (ARM), Gene Expression data.
M. Anandhavalli, Dr. M. K. Ghose, Department of Computer Science and Engineering, Sikkim Manipal Institute of Technology, Majitar, East Sikkim, India - 737136. e-mail: anandhigautham@gmail.com.
Dr. K. Gauthaman Department of Pharmacognosy, Himalayan Pharmacy Institute, Majitar, East Sikkim-737136, India
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Cite: M. Anandhavalli, M. K. Ghose, K. Gauthaman, "Association Rule Mining in Genomics,"
International Journal of Computer Theory and Engineering vol. 2, no. 2, pp. 269-273, 2010.
