570974
doi
10.2312/3dor.20171055
oai:zenodo.org:570974
user-eu
Craciun, Daniela
Laboratoire GBA EA4627, Conservatoire National des Arts et Métiers, 2 rue Conté, 75003 Paris, France
Christoffer, Charles
Department of Computer Science, Purdue University, 305 N. University St., West Lafayette, IN 47907, USA
Han, Xusi
Department of Biological Sciences, Purdue University, 249 S. Martin Jischke Dr., West Lafayette, IN 47907, USA
Kihara, Daisuke
Department of Computer Science, Purdue University, 305 N. University St., West Lafayette, IN 47907, USA
Levieux, Guillaume
Laboratoire CEDRIC EA4647, Conservatoire National des Arts et Métiers, 2 rue Conté, 75003, Paris, France
Montes, Matthieu
Laboratoire GBA EA4627, Conservatoire National des Arts et Métiers, 2 rue Conté, 75003 Paris, France
Qin, Hong
Computer Science Dept., Stony Brook University, Stony Brook, NY 11794, USA
Sahu, Pranjal
Computer Science Dept., Stony Brook University, Stony Brook, NY 11794, USA
Terashi, Genki
Department of Biological Sciences, Purdue University, 249 S. Martin Jischke Dr., West Lafayette, IN 47907, USA
Liu, Haiguang
Complex Systems Division, Beijing Computational Science Research Center, Z-Park II, Haidian, Beijing, China 100193
SHREC'17 Track: Protein Shape Retrieval
Song, Na
Complex Systems Division, Beijing Computational Science Research Center, Z-Park II, Haidian, Beijing, China 100193
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
SHREC
3D Object Retrieval
Protein shape
<p>The large number of protein structures deposited in the protein database provide an opportunity to examine the structure relations using computational algorithms, which can be used to classify the structures based on shape similarity. In this paper, we report the result of the SHREC 2017 track on shape retrievals from protein database. The goal of this track is to test the performance of the algorithms proposed by participants for the retrieval of bioshape (proteins). The test set is composed of 5,854 abstracted shapes from actual protein structures after removing model redundancy. Ten query shapes were selected from a set of representative molecules that have important biological functions. Six methods from four teams were evaluated and the performance is summarized in this report, in which both the retrieval accuracy and computational speed were compared. The biological relevance of the shape retrieval approaches is discussed. We also discussed the future perspectives of shape retrieval for biological molecular models.</p>
Zenodo
2017-05-03
info:eu-repo/semantics/conferencePaper
799088
user-eu
award_title=2D Conformal mapping of protein surfaces: applications to VIsualization and DOCKing software; award_number=640283; award_identifiers_scheme=url; award_identifiers_identifier=https://cordis.europa.eu/projects/640283; funder_id=00k4n6c32; funder_name=European Commission;
1579535202.734547
1227231
md5:8d125b3accba4e0702ae5e164617dc8b
https://zenodo.org/records/570974/files/Song_SHREC2017.pdf
public