January 27, 2021 Conference paper Open Access

Alexander Chervov; Andrei Zinovyev

DCAT Export

<?xml version='1.0' encoding='utf-8'?>
<rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:adms="http://www.w3.org/ns/adms#" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:dct="http://purl.org/dc/terms/" xmlns:dctype="http://purl.org/dc/dcmitype/" xmlns:dcat="http://www.w3.org/ns/dcat#" xmlns:duv="http://www.w3.org/ns/duv#" xmlns:foaf="http://xmlns.com/foaf/0.1/" xmlns:frapo="http://purl.org/cerif/frapo/" xmlns:geo="http://www.w3.org/2003/01/geo/wgs84_pos#" xmlns:gsp="http://www.opengis.net/ont/geosparql#" xmlns:locn="http://www.w3.org/ns/locn#" xmlns:org="http://www.w3.org/ns/org#" xmlns:owl="http://www.w3.org/2002/07/owl#" xmlns:prov="http://www.w3.org/ns/prov#" xmlns:rdfs="http://www.w3.org/2000/01/rdf-schema#" xmlns:schema="http://schema.org/" xmlns:skos="http://www.w3.org/2004/02/skos/core#" xmlns:vcard="http://www.w3.org/2006/vcard/ns#" xmlns:wdrs="http://www.w3.org/2007/05/powder-s#">
  <rdf:Description rdf:about="https://doi.org/10.5281/zenodo.5782816">
    <dct:identifier rdf:datatype="http://www.w3.org/2001/XMLSchema#anyURI">https://doi.org/10.5281/zenodo.5782816</dct:identifier>
    <foaf:page rdf:resource="https://doi.org/10.5281/zenodo.5782816"/>
    <dct:creator>
      <rdf:Description>
        <rdf:type rdf:resource="http://xmlns.com/foaf/0.1/Agent"/>
        <foaf:name>Alexander Chervov</foaf:name>
        <org:memberOf>
          <foaf:Organization>
            <foaf:name>Institut Curie</foaf:name>
          </foaf:Organization>
        </org:memberOf>
      </rdf:Description>
    </dct:creator>
    <dct:creator>
      <rdf:Description>
        <rdf:type rdf:resource="http://xmlns.com/foaf/0.1/Agent"/>
        <foaf:name>Andrei Zinovyev</foaf:name>
        <org:memberOf>
          <foaf:Organization>
            <foaf:name>Institut Curie</foaf:name>
          </foaf:Organization>
        </org:memberOf>
      </rdf:Description>
    </dct:creator>
    <dct:title>Clinical trajectories estimated from bulk tumoral molecular proles using elastic principal trees</dct:title>
    <dct:publisher>
      <foaf:Agent>
        <foaf:name>Zenodo</foaf:name>
      </foaf:Agent>
    </dct:publisher>
    <dct:issued rdf:datatype="http://www.w3.org/2001/XMLSchema#gYear">2021</dct:issued>
    <dcat:keyword>clinical trajectories,</dcat:keyword>
    <dcat:keyword>breast cancer</dcat:keyword>
    <dcat:keyword>transcriptome</dcat:keyword>
    <dcat:keyword>principal tree</dcat:keyword>
    <dcat:keyword>survival analysis</dcat:keyword>
    <frapo:isFundedBy rdf:resource="info:eu-repo/grantAgreement/EC/H2020/826121/"/>
    <schema:funder>
      <foaf:Organization>
        <dct:identifier rdf:datatype="http://www.w3.org/2001/XMLSchema#string">10.13039/100010661</dct:identifier>
        <foaf:name>European Commission</foaf:name>
      </foaf:Organization>
    </schema:funder>
    <frapo:isFundedBy rdf:resource="info:eu-repo/grantAgreement/ANR//ANR-19-P3IA-0001/"/>
    <schema:funder>
      <foaf:Organization>
        <dct:identifier rdf:datatype="http://www.w3.org/2001/XMLSchema#string">10.13039/501100001665</dct:identifier>
        <foaf:name>Agence Nationale de la Recherche</foaf:name>
      </foaf:Organization>
    </schema:funder>
    <dct:issued rdf:datatype="http://www.w3.org/2001/XMLSchema#date">2021-01-27</dct:issued>
    <dct:language rdf:resource="http://publications.europa.eu/resource/authority/language/ENG"/>
    <owl:sameAs rdf:resource="https://zenodo.org/record/5782816"/>
    <adms:identifier>
      <adms:Identifier>
        <skos:notation rdf:datatype="http://www.w3.org/2001/XMLSchema#anyURI">https://zenodo.org/record/5782816</skos:notation>
        <adms:schemeAgency>url</adms:schemeAgency>
      </adms:Identifier>
    </adms:identifier>
    <dct:isVersionOf rdf:resource="https://doi.org/10.5281/zenodo.5782815"/>
    <dct:isPartOf rdf:resource="https://zenodo.org/communities/ipc"/>
    <dct:description>&lt;p&gt;Clinical trajectory is a clinically relevant sequence of ordered patient phenotypes representing consecutive states of a developing disease and leading to some final state. Extracting trajectories from large scale&amp;nbsp;medical data is of great interest for dynamical phenotyping of various diseases but remains a challenge for&amp;nbsp;machine learning methods, especially in the case of synchronic (with short follow up) observations. Here&amp;nbsp;we describe an approach for trajectory-based analysis of cancer data using elastic principal trees and test&amp;nbsp;it on a large collection of molecular tumoral profiles for breast cancer. We show that the disease progress&amp;nbsp;quantified with pseudotime (the geodesic distance from the root) along a particular trajectory can serve&amp;nbsp;as a significant prognostic factor, not redundant with gene expression-based predictors. We conclude that&amp;nbsp;application of the elastic principal trees to transcriptomic data can be of interest for clinical applications.&lt;/p&gt;</dct:description>
    <dct:accessRights rdf:resource="http://publications.europa.eu/resource/authority/access-right/PUBLIC"/>
    <dct:accessRights>
      <dct:RightsStatement rdf:about="info:eu-repo/semantics/openAccess">
        <rdfs:label>Open Access</rdfs:label>
      </dct:RightsStatement>
    </dct:accessRights>
    <dct:license rdf:resource="https://creativecommons.org/licenses/by/4.0/legalcode"/>
    <dcat:distribution>
      <dcat:Distribution>
        <dcat:accessURL rdf:resource="https://doi.org/10.5281/zenodo.5782816"/>
        <dcat:byteSize>1446940</dcat:byteSize>
        <dcat:downloadURL rdf:resource="https://zenodo.org/record/5782816/files/ChervovZinovyev_BulkOmics2021.pdf"/>
        <dcat:mediaType>application/pdf</dcat:mediaType>
      </dcat:Distribution>
    </dcat:distribution>
  </rdf:Description>
  <foaf:Project rdf:about="info:eu-repo/grantAgreement/EC/H2020/826121/">
    <dct:identifier rdf:datatype="http://www.w3.org/2001/XMLSchema#string">826121</dct:identifier>
    <dct:title>individualizedPaediatricCure: Cloud-based virtual-patient models for precision paediatric oncology</dct:title>
    <frapo:isAwardedBy>
      <foaf:Organization>
        <dct:identifier rdf:datatype="http://www.w3.org/2001/XMLSchema#string">10.13039/100010661</dct:identifier>
        <foaf:name>European Commission</foaf:name>
      </foaf:Organization>
    </frapo:isAwardedBy>
  </foaf:Project>
  <foaf:Project rdf:about="info:eu-repo/grantAgreement/ANR//ANR-19-P3IA-0001/">
    <dct:identifier rdf:datatype="http://www.w3.org/2001/XMLSchema#string">ANR-19-P3IA-0001</dct:identifier>
    <dct:title>PaRis Artificial Intelligence Research InstitutE</dct:title>
    <frapo:isAwardedBy>
      <foaf:Organization>
        <dct:identifier rdf:datatype="http://www.w3.org/2001/XMLSchema#string">10.13039/501100001665</dct:identifier>
        <foaf:name>Agence Nationale de la Recherche</foaf:name>
      </foaf:Organization>
    </frapo:isAwardedBy>
  </foaf:Project>
</rdf:RDF>