November 22, 2017 Conference paper Open Access

Craciun, Daniela; Sirugue, Jeremy; Montes, Matthieu

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  <identifier identifierType="URL">https://zenodo.org/record/1167593</identifier>
  <creators>
    <creator>
      <creatorName>Craciun, Daniela</creatorName>
      <givenName>Daniela</givenName>
      <familyName>Craciun</familyName>
      <affiliation>Laboratoire GBA, EA4627, CNAM</affiliation>
    </creator>
    <creator>
      <creatorName>Sirugue, Jeremy</creatorName>
      <givenName>Jeremy</givenName>
      <familyName>Sirugue</familyName>
      <affiliation>Laboratoire GBA, EA4627, CNAM</affiliation>
    </creator>
    <creator>
      <creatorName>Montes, Matthieu</creatorName>
      <givenName>Matthieu</givenName>
      <familyName>Montes</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0001-5921-460X</nameIdentifier>
      <affiliation>Laboratoire GBA, EA4627, CNAM</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Global-to-local protein shape similarity system driven by digital elevation models</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2017</publicationYear>
  <subjects>
    <subject>Shape similarity search, Protein structure, Computer vision</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2017-11-22</date>
  </dates>
  <resourceType resourceTypeGeneral="ConferencePaper"/>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/1167593</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.1109/BIOSMART.2017.8095317</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;We are currently developing a bio-shape similarity system for supplying high-throughput protein shape similarity applications within massive datasets. The proposed system is powered by a global-to-local shape similarity system which exploits shape elevation and local convexity attributes. In the first step, a global similarity is computed between the shape descriptors associated to each protein input. The procedure outputs best N similarities chosen by the user, within a query-to-cluster approach. The second stage is a patch-based local similarity computation method which is designed to find the best similar target from the cluster for supplying query-to-target protein retrieval applications. The local patch-based similarity comparison benefits of a multi-CPU implementation, offering thus fast query search capabilities within massive datasets. Experimental results on the SHREC 2017 BioShape dataset composed of 5484 models, illustrate the effectiveness of the proposed system.&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/H2020/640283/">640283</awardNumber>
      <awardTitle>2D Conformal mapping of protein surfaces: applications to VIsualization and DOCKing software</awardTitle>
    </fundingReference>
  </fundingReferences>
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