1 Ocak 2020 Dergi makalesi Açık Erişim

Rahimi, Arezou; Gonen, Mehmet

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  <identifier identifierType="URL">https://aperta.ulakbim.gov.tr/record/3801</identifier>
  <creators>
    <creator>
      <creatorName>Rahimi, Arezou</creatorName>
      <givenName>Arezou</givenName>
      <familyName>Rahimi</familyName>
      <affiliation>Koc Univ, Grad Sch Sci &amp; Engn, TR-34450 Istanbul, Turkey</affiliation>
    </creator>
    <creator>
      <creatorName>Gonen, Mehmet</creatorName>
      <givenName>Mehmet</givenName>
      <familyName>Gonen</familyName>
    </creator>
  </creators>
  <titles>
    <title>A Multitask Multiple Kernel Learning Formulation For Discriminating Early- And Late-Stage Cancers</title>
  </titles>
  <publisher>Aperta</publisher>
  <publicationYear>2020</publicationYear>
  <dates>
    <date dateType="Issued">2020-01-01</date>
  </dates>
  <resourceType resourceTypeGeneral="Text">Journal article</resourceType>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://aperta.ulakbim.gov.tr/record/3801</alternateIdentifier>
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  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.1093/bioinformatics/btaa168</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="http://www.opendefinition.org/licenses/cc-by">Creative Commons Attribution</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
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  <descriptions>
    <description descriptionType="Abstract">Motivation: Genomic information is increasingly being used in diagnosis, prognosis and treatment of cancer. The severity of the disease is usually measured by the tumor stage. Therefore, identifying pathways playing an important role in progression of the disease stage is of great interest. Given that there are similarities in the underlying mechanisms of different cancers, in addition to the considerable correlation in the genomic data, there is a need for machine learning methods that can take these aspects of genomic data into account. Furthermore, using machine learning for studying multiple cancer cohorts together with a collection of molecular pathways creates an opportunity for knowledge extraction.</description>
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