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<pubDate>Wed, 20 Aug 2008 22:12:55 BST</pubDate>


	<title>CiteULike: heliopais Butte</title>
	<description>CiteULike: heliopais Butte</description>


	<link>http://www.citeulike.org/user/heliopais/author/Butte</link>
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    <title>Mutual information relevance networks: functional genomic clustering using pairwise entropy measurements.</title>
    <link>http://www.citeulike.org/user/heliopais/article/2239283</link>
    <description>&lt;i&gt;Pac Symp Biocomput (2000), pp. 418-429.&lt;/i&gt;&lt;br /&gt;&lt;br /&gt;Increasing numbers of methodologies are available to find functional genomic clusters in RNA expression data. We describe a technique that computes comprehensive pair-wise mutual information for all genes in such a data set. An association with a high mutual information means that one gene is non-randomly associated with another; we hypothesize this means the two are related biologically. By picking a threshold mutual information and using only associations at or above the threshold, we show how this technique was used on a public data set of 79 RNA expression measurements of 2,467 genes to construct 22 clusters, or Relevance Networks. The biological significance of each Relevance Network is explained.</description>
    <dc:title>Mutual information relevance networks: functional genomic clustering using pairwise entropy measurements.</dc:title>

    <dc:creator>AJ Butte</dc:creator>
    <dc:creator>IS Kohane</dc:creator>
    <dc:source>Pac Symp Biocomput (2000), pp. 418-429.</dc:source>
    <dc:date>2008-01-16T13:44:38-00:00</dc:date>
    <prism:publicationYear>2000</prism:publicationYear>
    <prism:publicationName>Pac Symp Biocomput</prism:publicationName>
    <prism:issn>1793-5091</prism:issn>
    <prism:startingPage>418</prism:startingPage>
    <prism:endingPage>429</prism:endingPage>
    <prism:category>genetic_regulatory_network</prism:category>
    <prism:category>information_theory</prism:category>
    <prism:category>network_inference</prism:category>
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