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Association Analysis Techniques for Discovering Functional Modules from Microarray Data

Gaurav Pandey¹, Gowtham Atluri¹, Michael Steinbach¹ & Vipin Kumar¹

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1. Department of Computer Science and Engineering, University of Minnesota, Twin Cities

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Document Type:

Manuscript

Date:

Received 13 August 2008 22:32 UTC; Posted 13 August 2008

Subjects:

Genetics & Genomics, Molecular Cell Biology, Bioinformatics

Tags:

• Microarray data
• Association analysis
• Functional modules
• Exhaustive search
• Efficient algorithms

Abstract:

An application of great interest in microarray data analysis is the identification of a group of genes that show very similar patterns of expression in a data set, and are expected to represent groups of genes that perform common/similar functions, also known as functional modules. Although clustering offers a natural solution to this problem, it suffers from the limitation that it uses all the conditions to compare two genes, whereas only a subset of them may be relevant. Association analysis offers an alternative route for finding such groups of genes that may be co-expressed only over a subset of the experimental conditions used to prepare the data set. The techniques in this field attempt to find groups of data objects that contain coherent values across a set of attributes, in an exhaustive and efficient manner. In this paper, we illustrate how a generalization of the techniques in association analysis for real-valued data can be utilized to extract coherent functional modules from large microarray data sets.

Collection:

AFP-Biosapiens 2008

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This document is licensed to the public under the Creative Commons Attribution 3.0 License

How to cite this document:

Pandey, Gaurav, Atluri, Gowtham, Steinbach, Michael, and Kumar, Vipin. Association Analysis Techniques for Discovering Functional Modules from Microarray Data. Available from Nature Precedings <http://dx.doi.org/10.1038/npre.2008.2184.1> (2008)

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