Journal article Open Access

Key aspects of analyzing microarray gene-expression data

Chen, James J.


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  <identifier identifierType="URL">https://zenodo.org/record/1236433</identifier>
  <creators>
    <creator>
      <creatorName>Chen, James J.</creatorName>
      <givenName>James J.</givenName>
      <familyName>Chen</familyName>
    </creator>
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  <titles>
    <title>Key aspects of analyzing microarray gene-expression data</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2007</publicationYear>
  <dates>
    <date dateType="Issued">2007-05-01</date>
  </dates>
  <resourceType resourceTypeGeneral="JournalArticle"/>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/1236433</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.2217/14622416.8.5.473</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="https://creativecommons.org/publicdomain/zero/1.0/legalcode">Creative Commons Zero v1.0 Universal</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">One major challenge with the use of microarray technology is the analysis of massive amounts of gene-expression data for various applications. This review addresses the key aspects of the microarray gene-expression data analysis for the two most common objectives: class comparison and class prediction. Class comparison mainly aims to select which genes are differentially expressed across experimental conditions. Gene selection is separated into two steps: gene ranking and assigning a significance level. Class prediction uses expression profiling analysis to develop a prediction model for patient selection, diagnostic prediction or prognostic classification. Development of a prediction model involves two components: model building and performance assessment. It also describes two additional data analysis methods: gene-class testing and multiple ordering criteria.</description>
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