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            <description>A name given to the resource</description>
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                <text>Coronavirus</text>
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            <name>Description</name>
            <description>An account of the resource</description>
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                <text>Dominio científico: Coronavirus</text>
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    <description>A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text.</description>
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      <name>Dublin Core</name>
      <description>The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/.</description>
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        <element elementId="50">
          <name>Title</name>
          <description>A name given to the resource</description>
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            <elementText elementTextId="22412">
              <text>Large-scale validation of methods for cytotoxic T-lymphocyte epitope prediction</text>
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        <element elementId="39">
          <name>Creator</name>
          <description>An entity primarily responsible for making the resource</description>
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            <elementText elementTextId="22413">
              <text>Lund Ole, Buus Søren, Lamberth Kasper, Lundegaard Claus, Larsen Mette V, Nielsen Morten</text>
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          <name>Description</name>
          <description>An account of the resource</description>
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              <text>Abstract Background Reliable predictions of Cytotoxic T lymphocyte (CTL) epitopes are essential for rational vaccine design. Most importantly, they can minimize the experimental effort needed to identify epitopes. NetCTL is a web-based tool designed for predicting human CTL epitopes in any given protein. It does so by integrating predictions of proteasomal cleavage, TAP transport efficiency, and MHC class I affinity. At least four other methods have been developed recently that likewise attempt to predict CTL epitopes: EpiJen, MAPPP, MHC-pathway, and WAPP. In order to compare the performance of prediction methods, objective benchmarks and standardized performance measures are needed. Here, we develop such large-scale benchmark and corresponding performance measures and report the performance of an updated version 1.2 of NetCTL in comparison with the four other methods. Results We define a number of performance measures that can handle the different types of output data from the five methods. We use two evaluation datasets consisting of known HIV CTL epitopes and their source proteins. The source proteins are split into all possible 9 mers and except for annotated epitopes; all other 9 mers are considered non-epitopes. In the RANK measure, we compare two methods at a time and count how often each of the methods rank the epitope highest. In another measure, we find the specificity of the methods at three predefined sensitivity values. Lastly, for each method, we calculate the percentage of known epitopes that rank within the 5% peptides with the highest predicted score. Conclusion NetCTL-1.2 is demonstrated to have a higher predictive performance than EpiJen, MAPPP, MHC-pathway, and WAPP on all performance measures. The higher performance of NetCTL-1.2 as compared to EpiJen and MHC-pathway is, however, not statistically significant on all measures. In the large-scale benchmark calculation consisting of 216 known HIV epitopes covering all 12 recognized HLA supertypes, the NetCTL-1.2 method was shown to have a sensitivity among the 5% top-scoring peptides above 0.72. On this dataset, the best of the other methods achieved a sensitivity of 0.64. The NetCTL-1.2 method is available at http://www.cbs.dtu.dk/services/NetCTL. All used datasets are available at http://www.cbs.dtu.dk/suppl/immunology/CTL-1.2.php.</text>
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          <name>Date</name>
          <description>A point or period of time associated with an event in the lifecycle of the resource</description>
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            <elementText elementTextId="22415">
              <text>2007</text>
            </elementText>
          </elementTextContainer>
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        <element elementId="43">
          <name>Identifier</name>
          <description>An unambiguous reference to the resource within a given context</description>
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            <elementText elementTextId="22416">
              <text>DOI: 10.1186/1471-2105-8-424</text>
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        <element elementId="48">
          <name>Source</name>
          <description>A related resource from which the described resource is derived</description>
          <elementTextContainer>
            <elementText elementTextId="22417">
              <text>BMC Bioinformatics</text>
            </elementText>
          </elementTextContainer>
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        <element elementId="45">
          <name>Publisher</name>
          <description>An entity responsible for making the resource available</description>
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            <elementText elementTextId="22418">
              <text>BMC</text>
            </elementText>
          </elementTextContainer>
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          <name>Coverage</name>
          <description>The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant</description>
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            <elementText elementTextId="22419">
              <text>Biology (General), Computer applications to medicine. Medical informatics</text>
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          <name>Language</name>
          <description>A language of the resource</description>
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            <elementText elementTextId="22420">
              <text>EN</text>
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