Poster Open Access

Interpretability for computational biology

Nguyen An-phi; Rodriguez-Martinez


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  <dc:creator>Nguyen An-phi</dc:creator>
  <dc:creator>Rodriguez-Martinez</dc:creator>
  <dc:date>2019-08-22</dc:date>
  <dc:description>Why do we need interpretability to unveil the decision process ofa machine learning model?
Trust - for high-risk scenarios, e.g. healthcare, the user needs to trust the decision taken.
Debugging - the model may be badly trained or there might be an unfair bias in either the dataset or the model itself.
Hypothesis generation - surprising results might be consequences of new mechanisms or patterns unknown even to field experts.</dc:description>
  <dc:identifier>https://zenodo.org/record/3374361</dc:identifier>
  <dc:identifier>10.5281/zenodo.3374361</dc:identifier>
  <dc:identifier>oai:zenodo.org:3374361</dc:identifier>
  <dc:relation>info:eu-repo/grantAgreement/EC/H2020/826121/</dc:relation>
  <dc:relation>doi:10.5281/zenodo.3374360</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>computational biology</dc:subject>
  <dc:title>Interpretability for computational biology</dc:title>
  <dc:type>info:eu-repo/semantics/conferencePoster</dc:type>
  <dc:type>poster</dc:type>
</oai_dc:dc>
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