August 22, 2019 Poster Open Access

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

DataCite XML Export

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  <identifier identifierType="DOI">10.5281/zenodo.3374375</identifier>
  <creators>
    <creator>
      <creatorName>Manica Matteo</creatorName>
    </creator>
    <creator>
      <creatorName>Oskooei Ali</creatorName>
    </creator>
    <creator>
      <creatorName>Born Jannis</creatorName>
    </creator>
    <creator>
      <creatorName>Subramanian Vigneshwari</creatorName>
    </creator>
    <creator>
      <creatorName>Saez-Rodriguez Julio</creatorName>
    </creator>
    <creator>
      <creatorName>Rodriguez Martinez Maria</creatorName>
    </creator>
  </creators>
  <titles>
    <title>Using attention-based neural networks to enable explainable drug sensitivity prediction on multimodal data</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2019</publicationYear>
  <subjects>
    <subject>drug sensitivity</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2019-08-22</date>
  </dates>
  <resourceType resourceTypeGeneral="Text">Poster</resourceType>
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    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/3374375</alternateIdentifier>
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    <rights rightsURI="https://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
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  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;PaccMann tackles the challenging problem of drug sensitivity prediction adopting a holistic approach.&lt;br&gt;
The model was trained on data from Genomics of Drug Sensitivity in Cancer (GDSC, https://www.cancerrxgene.org/)&lt;/p&gt;</description>
  </descriptions>
  <fundingReferences>
    <fundingReference>
      <funderName>European Commission</funderName>
      <funderIdentifier funderIdentifierType="Crossref Funder ID">10.13039/100010661</funderIdentifier>
      <awardNumber awardURI="info:eu-repo/grantAgreement/EC/H2020/826121/">826121</awardNumber>
      <awardTitle>individualizedPaediatricCure: Cloud-based virtual-patient models for precision paediatric oncology</awardTitle>
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