Presentation Open Access

How in silico methods and data reuse can help reduce the number of animal experiments

Maddalena Fratelli


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  <identifier identifierType="DOI">10.5281/zenodo.4524007</identifier>
  <creators>
    <creator>
      <creatorName>Maddalena Fratelli</creatorName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0002-1769-3427</nameIdentifier>
      <affiliation>Istituto di Ricerche Farmacologiche Mario Negri IRCCS</affiliation>
    </creator>
  </creators>
  <titles>
    <title>How in silico methods and data reuse can help reduce the number of animal experiments</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2021</publicationYear>
  <subjects>
    <subject>machine learning, in silico methods, data re.use, European Open Science Cloud, external validity</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2021-02-09</date>
  </dates>
  <language>en</language>
  <resourceType resourceTypeGeneral="Text">Presentation</resourceType>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/4524007</alternateIdentifier>
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  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.4524006</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;Presentation at the conference&amp;nbsp;&amp;nbsp;&amp;quot;Towards replacement of animals for scientific purposes&amp;quot; ,&amp;nbsp; 2-3 february 2021.&lt;/p&gt;

&lt;p&gt;Talking about machine learning to develop models of drug sensitivity prediction, (Q)SAR models of toxicity prediction and the role of data re-use in enhancing external validity of the studies.&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/H2020/824087/">824087</awardNumber>
      <awardTitle>Providing an open collaborative space for digital biology in Europe</awardTitle>
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