August 22, 2019 Poster Open Access

Manica Matteo; Oskooei Ali; Born Jannis; Subramanian Vigneshwari; Saez-Rodriguez Julio; Rodriguez Martinez Maria

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  <dc:creator>Manica Matteo</dc:creator>
  <dc:creator>Oskooei Ali</dc:creator>
  <dc:creator>Born Jannis</dc:creator>
  <dc:creator>Subramanian Vigneshwari</dc:creator>
  <dc:creator>Saez-Rodriguez Julio</dc:creator>
  <dc:creator>Rodriguez Martinez Maria</dc:creator>
  <dc:date>2019-08-22</dc:date>
  <dc:description>PaccMann tackles the challenging problem of drug sensitivity prediction adopting a holistic approach.
The model was trained on data from Genomics of Drug Sensitivity in Cancer (GDSC, https://www.cancerrxgene.org/)</dc:description>
  <dc:identifier>https://zenodo.org/record/3374375</dc:identifier>
  <dc:identifier>10.5281/zenodo.3374375</dc:identifier>
  <dc:identifier>oai:zenodo.org:3374375</dc:identifier>
  <dc:relation>info:eu-repo/grantAgreement/EC/H2020/826121/</dc:relation>
  <dc:relation>doi:10.5281/zenodo.3374374</dc:relation>
  <dc:relation>url:https://zenodo.org/communities/ipc</dc:relation>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:rights>https://creativecommons.org/licenses/by/4.0/legalcode</dc:rights>
  <dc:subject>drug sensitivity</dc:subject>
  <dc:title>Using attention-based neural networks to enable explainable drug sensitivity prediction on multimodal data</dc:title>
  <dc:type>info:eu-repo/semantics/conferencePoster</dc:type>
  <dc:type>poster</dc:type>
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