November 22, 2017 Conference paper Open Access

Craciun, Daniela; Sirugue, Jeremy; Montes, Matthieu

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{
  "DOI": "10.1109/BIOSMART.2017.8095317", 
  "author": [
    {
      "family": "Craciun, Daniela"
    }, 
    {
      "family": "Sirugue, Jeremy"
    }, 
    {
      "family": "Montes, Matthieu"
    }
  ], 
  "issued": {
    "date-parts": [
      [
        2017, 
        11, 
        22
      ]
    ]
  }, 
  "abstract": "<p>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.</p>", 
  "title": "Global-to-local protein shape similarity system driven by digital elevation models", 
  "type": "paper-conference", 
  "id": "1167593"
}