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        evaluation of the quality of genomic or proteomic data and computational methods
        is vital to our ability to use them for formulating novel biological hypotheses
        and directing further experiments. There is currently no standard approach
        to evaluation in functional genomics. Our analysis of existing approaches
        shows that they are inconsistent and contain substantial functional biases
        that render the resulting evaluations misleading both quantitatively and qualitatively.
        These problems make it essentially impossible to compare computational methods
        or large-scale experimental datasets and also result in conclusions that generalize
        poorly in most biological applications.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Results<\/jats:title>\n            <jats:p>We
        reveal issues with current evaluation methods here and suggest new approaches
        to evaluation that facilitate accurate and representative characterization
        of genomic methods and data. Specifically, we describe a functional genomics
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        how much we, as a community, are able to learn from the wealth of available
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        \               <full_title>BMC Genomics</full_title>\r\n                <abbrev_title>BMC
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        \                   <given_name>Daniel R</given_name>\r\n                    <surname>Barrett</surname>\r\n
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        \                 <jats:sec>\r\n                    <jats:title>Background</jats:title>\r\n
        \                   <jats:p>Accurate evaluation of the quality of genomic
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        them for formulating novel biological hypotheses and directing further experiments.
        There is currently no standard approach to evaluation in functional genomics.
        Our analysis of existing approaches shows that they are inconsistent and contain
        substantial functional biases that render the resulting evaluations misleading
        both quantitatively and qualitatively. These problems make it essentially
        impossible to compare computational methods or large-scale experimental datasets
        and also result in conclusions that generalize poorly in most biological applications.</jats:p>\r\n
        \                 </jats:sec>\r\n                  <jats:sec>\r\n                    <jats:title>Results</jats:title>\r\n
        \                   <jats:p>We reveal issues with current evaluation methods
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        \                 <jats:sec>\r\n                    <jats:title>Conclusion</jats:title>\r\n
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