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

Maddalena Fratelli


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  <dc:creator>Maddalena Fratelli</dc:creator>
  <dc:date>2021-02-09</dc:date>
  <dc:description>Presentation at the conference  "Towards replacement of animals for scientific purposes" ,  2-3 february 2021.

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.</dc:description>
  <dc:identifier>https://zenodo.org/record/4524007</dc:identifier>
  <dc:identifier>10.5281/zenodo.4524007</dc:identifier>
  <dc:identifier>oai:zenodo.org:4524007</dc:identifier>
  <dc:language>eng</dc:language>
  <dc:relation>info:eu-repo/grantAgreement/EC/H2020/824087/</dc:relation>
  <dc:relation>doi:10.5281/zenodo.4524006</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>machine learning, in silico methods, data re.use, European Open Science Cloud, external validity</dc:subject>
  <dc:title>How in silico methods and data reuse can help reduce the number of animal experiments</dc:title>
  <dc:type>info:eu-repo/semantics/lecture</dc:type>
  <dc:type>presentation</dc:type>
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