August 22, 2019 Presentation Open Access

Cadow Joris

Dublin Core Export

<?xml version='1.0' encoding='utf-8'?>
<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
  <dc:creator>Cadow Joris</dc:creator>
  <dc:date>2019-08-22</dc:date>
  <dc:description>Roadmap

Molecular data classification: use network topology as a regulariser, define meta-features using pathway information Pathway-Induced. Multiple Kernel Learning (PIMKL): concept, pathway induction, multiple kernel learning PIMKL benchmarking: benchmark against other prior knowledge informed methods on multiple breast cancer cohorts.
PIMKL application: detects tumor samples accurately, integrates multiple omics seamlessly, identifies relevant pathways for each data type.</dc:description>
  <dc:identifier>https://zenodo.org/record/3374393</dc:identifier>
  <dc:identifier>10.5281/zenodo.3374393</dc:identifier>
  <dc:identifier>oai:zenodo.org:3374393</dc:identifier>
  <dc:relation>info:eu-repo/grantAgreement/EC/H2020/668858/</dc:relation>
  <dc:relation>doi:10.5281/zenodo.3374392</dc:relation>
  <dc:relation>url:https://zenodo.org/communities/ipc</dc:relation>
  <dc:relation>url:https://zenodo.org/communities/precise</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>molecular measurements</dc:subject>
  <dc:title>Interpretable classification of molecular measurements via pathway-induced multiple kernel learning</dc:title>
  <dc:type>info:eu-repo/semantics/lecture</dc:type>
  <dc:type>presentation</dc:type>
</oai_dc:dc>