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    <title>Nature Precedings - Tag feed for Association analysis</title>
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      <title>Association Analysis Techniques for Discovering Functional Modules from Microarray Data</title>
      <link>http://dx.doi.org/10.1038/npre.2008.2184.1</link>
      <description>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.</description>
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      <pubDate>Wed, 13 Aug 2008 22:42:34 UTC</pubDate>
      <dc:title>Association Analysis Techniques for Discovering Functional Modules from Microarray Data</dc:title>
      <dc:identifier>doi:10.1038/npre.2008.2184.1</dc:identifier>
      <dc:date>2008-08-13</dc:date>
      <dc:creator>Gaurav Pandey</dc:creator>
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      <prism:publicationDate>2008-08-13T22:42:34Z</prism:publicationDate>
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      <prism:section>Genetics &amp; Genomics</prism:section>
      <prism:section>Molecular Cell Biology</prism:section>
      <prism:section>Bioinformatics</prism:section>
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