1 Ocak 2020 Dergi makalesi Açık Erişim

Unsal, Serbulent; Acar, Aybar; Itik, Mehmet; Kabatas, Ayse; Gedikli, Oznur; Ozdemir, Feyyaz; Turhan, Kemal

Dublin Core

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  <dc:creator>Unsal, Serbulent</dc:creator>
  <dc:creator>Acar, Aybar</dc:creator>
  <dc:creator>Itik, Mehmet</dc:creator>
  <dc:creator>Kabatas, Ayse</dc:creator>
  <dc:creator>Gedikli, Oznur</dc:creator>
  <dc:creator>Ozdemir, Feyyaz</dc:creator>
  <dc:creator>Turhan, Kemal</dc:creator>
  <dc:date>2020-01-01</dc:date>
  <dc:description>Purpose: Cancer is one of the most complex phenomena in biology and medicine. Extensive attempts have been made to work around this complexity. In this study, we try to take a selective approach; not modeling each particular facet in detail but rather only the pertinent and essential parts of the tumor system are simulated and followed by optimization, revealing specific traits. This leads us to a pellucid personalized model which is noteworthy as it closely approximates existing experimental results.</dc:description>
  <dc:identifier>https://aperta.ulakbim.gov.trrecord/5929</dc:identifier>
  <dc:identifier>oai:zenodo.org:5929</dc:identifier>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:rights>http://www.opendefinition.org/licenses/cc-by</dc:rights>
  <dc:source>JOURNAL OF BASIC AND CLINICAL HEALTH SCIENCES 4(3) 347-363</dc:source>
  <dc:title>Personalized Tumor Growth Prediction Using Multiscale Modeling</dc:title>
  <dc:type>info:eu-repo/semantics/article</dc:type>
  <dc:type>publication-article</dc:type>
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