August 22, 2019 Poster Open Access

Manica Matteo; Cadow Joris; Mathis Roland; Rodriguez Martinez Maria

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<resource xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns="http://datacite.org/schema/kernel-4" xsi:schemaLocation="http://datacite.org/schema/kernel-4 http://schema.datacite.org/meta/kernel-4.1/metadata.xsd">
  <identifier identifierType="DOI">10.5281/zenodo.3374413</identifier>
  <creators>
    <creator>
      <creatorName>Manica Matteo</creatorName>
    </creator>
    <creator>
      <creatorName>Cadow Joris</creatorName>
    </creator>
    <creator>
      <creatorName>Mathis Roland</creatorName>
    </creator>
    <creator>
      <creatorName>Rodriguez Martinez Maria</creatorName>
    </creator>
  </creators>
  <titles>
    <title>PIMKL: Pathway Induced Multiple Kernel Learning</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2019</publicationYear>
  <subjects>
    <subject>multiple kernel learning</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2019-08-22</date>
  </dates>
  <resourceType resourceTypeGeneral="Text">Poster</resourceType>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/3374413</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.3374412</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://zenodo.org/communities/ipc</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://zenodo.org/communities/precise</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;What&lt;/p&gt;

&lt;p&gt;&amp;bull; Accurate phenotype classification&lt;br&gt;
&amp;bull; Biological interpretability&lt;br&gt;
&amp;bull; Robustness to noise&lt;br&gt;
&amp;bull; Handling curse of dimensionality&lt;/p&gt;

&lt;p&gt;How&lt;/p&gt;

&lt;p&gt;Exploit prior knowledge from biological&lt;br&gt;
networks&lt;br&gt;
&amp;bull; Apply multiple kernel learning for&lt;br&gt;
feature encoding&lt;br&gt;
&amp;bull; Use pathway annotations to enable&lt;br&gt;
interpretability&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/H2020/668858/">668858</awardNumber>
      <awardTitle>PERSONALIZED ENGINE FOR CANCER INTEGRATIVE STUDY AND EVALUATION</awardTitle>
    </fundingReference>
  </fundingReferences>
</resource>