November 22, 2017 Conference paper Open Access

Craciun, Daniela; Sirugue, Jeremy; Montes, Matthieu

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    "description": "<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>", 
    "license": {
      "id": "CC-BY-4.0"
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    "title": "Global-to-local protein shape similarity system driven by digital elevation models", 
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        "title": "2D Conformal mapping of protein surfaces: applications to VIsualization and DOCKing software", 
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        "program": "H2020", 
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    "keywords": [
      "Shape similarity search, Protein structure, Computer vision"
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    "publication_date": "2017-11-22", 
    "creators": [
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        "affiliation": "Laboratoire GBA, EA4627, CNAM", 
        "name": "Craciun, Daniela"
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        "affiliation": "Laboratoire GBA, EA4627, CNAM", 
        "name": "Sirugue, Jeremy"
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        "affiliation": "Laboratoire GBA, EA4627, CNAM", 
        "name": "Montes, Matthieu"
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