Journal article Open Access

PaccMann: a web service for interpretable anticancer compound sensitivity prediction

Cadow, Joris; Born, Jannis; Manica, Matteo; Oskooei, Ali; Rodríguez Martínez, María


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  <identifier identifierType="URL">https://zenodo.org/record/3935564</identifier>
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    <creator>
      <creatorName>Cadow, Joris</creatorName>
      <givenName>Joris</givenName>
      <familyName>Cadow</familyName>
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    <creator>
      <creatorName>Born, Jannis</creatorName>
      <givenName>Jannis</givenName>
      <familyName>Born</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0001-8307-5670</nameIdentifier>
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    <creator>
      <creatorName>Manica, Matteo</creatorName>
      <givenName>Matteo</givenName>
      <familyName>Manica</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0002-8872-0269</nameIdentifier>
    </creator>
    <creator>
      <creatorName>Oskooei, Ali</creatorName>
      <givenName>Ali</givenName>
      <familyName>Oskooei</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0002-8318-687X</nameIdentifier>
    </creator>
    <creator>
      <creatorName>Rodríguez Martínez, María</creatorName>
      <givenName>María</givenName>
      <familyName>Rodríguez Martínez</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0003-3766-4233</nameIdentifier>
    </creator>
  </creators>
  <titles>
    <title>PaccMann: a web service for interpretable anticancer compound sensitivity prediction</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2020</publicationYear>
  <dates>
    <date dateType="Issued">2020-05-13</date>
  </dates>
  <language>en</language>
  <resourceType resourceTypeGeneral="JournalArticle"/>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/3935564</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.1093/nar/gkaa327</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://zenodo.org/communities/ipc</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <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>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;The identification of new targeted and personalized therapies for cancer requires the fast and accurate assessment of the drug efficacy of potential compounds against a particular biomolecular sample. It has been suggested that the integration of complementary sources of information might strengthen the accuracy of a drug efficacy prediction model. Here, we present a web-based platform for the Prediction of AntiCancer Compound sensitivity with Multimodal Attention-based Neural Networks (PaccMann). PaccMann is trained on public transcriptomic cell line profiles, compound structure information and drug sensitivity screenings, and outperforms state-of-the-art methods on anticancer drug sensitivity prediction. On the open-access web service (https://ibm.biz/paccmann-aas), users can select a known drug compound or design their own compound structure in an interactive editor, perform in-silico drug testing and investigate compound efficacy on publicly available or user-provided transcriptomic profiles. PaccMann leverages methods for model interpretability and outputs confidence scores as well as attention heatmaps that highlight the genes and chemical sub-structures that were more important to make a prediction, hence facilitating the understanding of the model&amp;rsquo;s decision making and the involved biochemical processes. We hope to serve the community with a toolbox for fast and efficient validation in drug repositioning or lead compound identification regimes.&lt;/p&gt;</description>
  </descriptions>
  <fundingReferences>
    <fundingReference>
      <funderName>European Commission</funderName>
      <funderIdentifier funderIdentifierType="Crossref Funder ID">10.13039/501100000780</funderIdentifier>
      <awardNumber awardURI="info:eu-repo/grantAgreement/EC/H2020/826121/">826121</awardNumber>
      <awardTitle>individualizedPaediatricCure: Cloud-based virtual-patient models for precision paediatric oncology</awardTitle>
    </fundingReference>
  </fundingReferences>
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