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<pubDate>Sat, 05 Jul 2008 13:12:57 BST</pubDate>


	<title>CiteULike: gsiwo's library [3 articles]</title>
	<description>CiteULike: gsiwo's library [3 articles]</description>


	<link>http://www.citeulike.org/user/gsiwo</link>
	<dc:publisher>CiteULike.org</dc:publisher>
	<dc:language>en-gb</dc:language>
	<dc:rights>Copyright &#169; 2004-2008 citeulike.org</dc:rights>
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        <rdf:li rdf:resource="http://www.citeulike.org/user/gsiwo/article/820297"/>
        <rdf:li rdf:resource="http://www.citeulike.org/user/gsiwo/article/1457666"/>
        <rdf:li rdf:resource="http://www.citeulike.org/user/gsiwo/article/1569195"/>

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<item rdf:about="http://www.citeulike.org/user/gsiwo/article/820297">
    <title>An introduction to ROC analysis</title>
    <link>http://www.citeulike.org/user/gsiwo/article/820297</link>
    <description>&lt;i&gt;Pattern Recognition Letters, Vol. 27, No. 8. (June 2006), pp. 861-874.&lt;/i&gt;&lt;br /&gt;&lt;br /&gt;Receiver operating characteristics (ROC) graphs are useful for organizing classifiers and visualizing their performance. ROC graphs are commonly used in medical decision making, and in recent years have been used increasingly in machine learning and data mining research. Although ROC graphs are apparently simple, there are some common misconceptions and pitfalls when using them in practice. The purpose of this article is to serve as an introduction to ROC graphs and as a guide for using them in research.</description>
    <dc:title>An introduction to ROC analysis</dc:title>

    <dc:creator>Tom Fawcett</dc:creator>
    <dc:identifier>doi:10.1016/j.patrec.2005.10.010</dc:identifier>
    <dc:source>Pattern Recognition Letters, Vol. 27, No. 8. (June 2006), pp. 861-874.</dc:source>
    <dc:date>2006-08-29T01:24:20-00:00</dc:date>
    <prism:publicationYear>2006</prism:publicationYear>
    <prism:publicationName>Pattern Recognition Letters</prism:publicationName>
    <prism:volume>27</prism:volume>
    <prism:number>8</prism:number>
    <prism:startingPage>861</prism:startingPage>
    <prism:endingPage>874</prism:endingPage>
    <prism:category>learning</prism:category>
    <prism:category>machine</prism:category>
    <prism:category>roc</prism:category>
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<item rdf:about="http://www.citeulike.org/user/gsiwo/article/1457666">
    <title>Machine Learning and Its Applications to Biology.</title>
    <link>http://www.citeulike.org/user/gsiwo/article/1457666</link>
    <description>&lt;i&gt;PLoS Comput Biol, Vol. 3, No. 6. (29 June 2007)&lt;/i&gt;</description>
    <dc:title>Machine Learning and Its Applications to Biology.</dc:title>

    <dc:creator>Adi L Tarca</dc:creator>
    <dc:creator>Vincent J Carey</dc:creator>
    <dc:creator>Xue-Wen Chen</dc:creator>
    <dc:creator>Roberto Romero</dc:creator>
    <dc:creator>Sorin Drăghici</dc:creator>
    <dc:identifier>doi:10.1371/journal.pcbi.0030116</dc:identifier>
    <dc:source>PLoS Comput Biol, Vol. 3, No. 6. (29 June 2007)</dc:source>
    <dc:date>2007-07-15T14:25:52-00:00</dc:date>
    <prism:publicationYear>2007</prism:publicationYear>
    <prism:publicationName>PLoS Comput Biol</prism:publicationName>
    <prism:issn>1553-7358</prism:issn>
    <prism:volume>3</prism:volume>
    <prism:number>6</prism:number>
    <prism:category>learning</prism:category>
    <prism:category>machine</prism:category>
</item>



<item rdf:about="http://www.citeulike.org/user/gsiwo/article/1569195">
    <title>A systematic bioinformatics approach for selection of epitope-based vaccine targets</title>
    <link>http://www.citeulike.org/user/gsiwo/article/1569195</link>
    <description>&lt;i&gt;Cellular Immunology, Vol. 244, No. 2. (December 2006), pp. 141-147.&lt;/i&gt;&lt;br /&gt;&lt;br /&gt;Epitope-based vaccines provide a new strategy for prophylactic and therapeutic application of pathogen-specific immunity. A critical requirement of this strategy is the identification and selection of T-cell epitopes that act as vaccine targets. This study describes current methodologies for the selection process, with dengue virus as a model system. A combination of publicly available bioinformatics algorithms and computational tools are used to screen and select antigen sequences as potential T-cell epitopes of supertype human leukocyte antigen (HLA) alleles. The selected sequences are tested for biological function by their activation of T-cells of HLA transgenic mice and of pathogen infected subjects. This approach provides an experimental basis for the design of pathogen specific, T-cell epitope-based vaccines that are targeted to majority of the genetic variants of the pathogen, and are effective for a broad range of differences in human leukocyte antigens among the global human population.</description>
    <dc:title>A systematic bioinformatics approach for selection of epitope-based vaccine targets</dc:title>

    <dc:creator>Asif Khan</dc:creator>
    <dc:creator>Olivo Miotto</dc:creator>
    <dc:creator>AT Heiny</dc:creator>
    <dc:creator>Jerome Salmon</dc:creator>
    <dc:creator>KN Srinivasan</dc:creator>
    <dc:creator>Eduardo Nascimento</dc:creator>
    <dc:creator>Marques</dc:creator>
    <dc:creator>Vladimir Brusic</dc:creator>
    <dc:creator>Tin Tan</dc:creator>
    <dc:creator>Thomas August</dc:creator>
    <dc:identifier>doi:10.1016/j.cellimm.2007.02.005</dc:identifier>
    <dc:source>Cellular Immunology, Vol. 244, No. 2. (December 2006), pp. 141-147.</dc:source>
    <dc:date>2007-08-16T13:20:41-00:00</dc:date>
    <prism:publicationYear>2006</prism:publicationYear>
    <prism:publicationName>Cellular Immunology</prism:publicationName>
    <prism:volume>244</prism:volume>
    <prism:number>2</prism:number>
    <prism:startingPage>141</prism:startingPage>
    <prism:endingPage>147</prism:endingPage>
    <prism:category>immunoinformatics</prism:category>
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