Conference paper Open Access

Global-to-local protein shape similarity system driven by digital elevation models

Craciun, Daniela; Sirugue, Jeremy; Montes, Matthieu

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<oai_dc:dc xmlns:dc="" xmlns:oai_dc="" xmlns:xsi="" xsi:schemaLocation="">
  <dc:creator>Craciun, Daniela</dc:creator>
  <dc:creator>Sirugue, Jeremy</dc:creator>
  <dc:creator>Montes, Matthieu</dc:creator>
  <dc:description>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.</dc:description>
  <dc:subject>Shape similarity search, Protein structure, Computer vision</dc:subject>
  <dc:title>Global-to-local protein shape similarity system driven by digital elevation models</dc:title>
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