1 Ocak 2020 Dergi makalesi Açık Erişim

Yalcin, Gizem Damla; Danisik, Nurseda; Baygin, Rana Can; Acar, Ahmet

MARC21 XML

<?xml version='1.0' encoding='UTF-8'?>
<record xmlns="http://www.loc.gov/MARC21/slim">
  <leader>00000nam##2200000uu#4500</leader>
  <datafield tag="700" ind1=" " ind2=" ">
    <subfield code="a">Danisik, Nurseda</subfield>
    <subfield code="u">Middle East Tech Univ, Dept Biol Sci, Univ Mah Dumlupinar Bulvari 1, TR-06800 Ankara, Turkey</subfield>
  </datafield>
  <datafield tag="700" ind1=" " ind2=" ">
    <subfield code="a">Baygin, Rana Can</subfield>
    <subfield code="u">Middle East Tech Univ, Dept Biol Sci, Univ Mah Dumlupinar Bulvari 1, TR-06800 Ankara, Turkey</subfield>
  </datafield>
  <datafield tag="700" ind1=" " ind2=" ">
    <subfield code="a">Acar, Ahmet</subfield>
    <subfield code="u">Middle East Tech Univ, Dept Biol Sci, Univ Mah Dumlupinar Bulvari 1, TR-06800 Ankara, Turkey</subfield>
  </datafield>
  <datafield tag="909" ind1="C" ind2="4">
    <subfield code="p">JOURNAL OF PERSONALIZED MEDICINE</subfield>
    <subfield code="v">10</subfield>
    <subfield code="n">4</subfield>
  </datafield>
  <datafield tag="980" ind1=" " ind2=" ">
    <subfield code="a">user-tubitak-destekli-proje-yayinlari</subfield>
  </datafield>
  <datafield tag="540" ind1=" " ind2=" ">
    <subfield code="a">Creative Commons Attribution</subfield>
    <subfield code="u">http://www.opendefinition.org/licenses/cc-by</subfield>
  </datafield>
  <datafield tag="024" ind1=" " ind2=" ">
    <subfield code="a">10.3390/jpm10040180</subfield>
    <subfield code="2">doi</subfield>
  </datafield>
  <datafield tag="245" ind1=" " ind2=" ">
    <subfield code="a">Systems Biology and Experimental Model Systems of Cancer</subfield>
  </datafield>
  <datafield tag="100" ind1=" " ind2=" ">
    <subfield code="a">Yalcin, Gizem Damla</subfield>
    <subfield code="u">Middle East Tech Univ, Dept Biol Sci, Univ Mah Dumlupinar Bulvari 1, TR-06800 Ankara, Turkey</subfield>
  </datafield>
  <datafield tag="909" ind1="C" ind2="O">
    <subfield code="o">oai:zenodo.org:11571</subfield>
    <subfield code="p">user-tubitak-destekli-proje-yayinlari</subfield>
  </datafield>
  <datafield tag="650" ind1="1" ind2="7">
    <subfield code="2">opendefinition.org</subfield>
    <subfield code="a">cc-by</subfield>
  </datafield>
  <datafield tag="260" ind1=" " ind2=" ">
    <subfield code="c">2020-01-01</subfield>
  </datafield>
  <datafield tag="856" ind1="4" ind2=" ">
    <subfield code="u">https://aperta.ulakbim.gov.trrecord/11571/files/bib-72514180-cdbe-4143-88e1-1ab8cbaf061f.txt</subfield>
    <subfield code="z">md5:d9021e45dba8fd59716775c346072c2f</subfield>
    <subfield code="s">153</subfield>
  </datafield>
  <datafield tag="542" ind1=" " ind2=" ">
    <subfield code="l">open</subfield>
  </datafield>
  <controlfield tag="005">20210315074155.0</controlfield>
  <controlfield tag="001">11571</controlfield>
  <datafield tag="980" ind1=" " ind2=" ">
    <subfield code="a">publication</subfield>
    <subfield code="b">article</subfield>
  </datafield>
  <datafield tag="520" ind1=" " ind2=" ">
    <subfield code="a">Over the past decade, we have witnessed an increasing number of large-scale studies that have provided multi-omics data by high-throughput sequencing approaches. This has particularly helped with identifying key (epi)genetic alterations in cancers. Importantly, aberrations that lead to the activation of signaling networks through the disruption of normal cellular homeostasis is seen both in cancer cells and also in the neighboring tumor microenvironment. Cancer systems biology approaches have enabled the efficient integration of experimental data with computational algorithms and the implementation of actionable targeted therapies, as the exceptions, for the treatment of cancer. Comprehensive multi-omics data obtained through the sequencing of tumor samples and experimental model systems will be important in implementing novel cancer systems biology approaches and increasing their efficacy for tailoring novel personalized treatment modalities in cancer. In this review, we discuss emerging cancer systems biology approaches based on multi-omics data derived from bulk and single-cell genomics studies in addition to existing experimental model systems that play a critical role in understanding (epi)genetic heterogeneity and therapy resistance in cancer.</subfield>
  </datafield>
</record>