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10.1371/journal.pone.0144578 http://scihub22266oqcxt.onion/10.1371/journal.pone.0144578 C4684231!4684231!26656740     free     free     free Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       PLoS+One 2015 ; 10 (12): ä Nephropedia Template TP {{tp|p=26656740|t=2015. Property Graph vs RDF Triple Store: A Comparison on Glycan Substructure Search |pdf=|usr=}}{{26656740}} gab.com Text Property Graph vs RDF Triple Store: A Comparison on Glycan Substructure Search JO: PLoS One http://www.kidney.de/pget.php?&tw=1&pmid=26656740 #FOAvip Resource description framework (RDF) and Property Graph databases are emerging technologies that are used for storing graph-structured data. We compare these technologies through a molecular biology use case: glycan substructure search. Glycans are branched tree-like molecules composed of building blocks linked together by chemical bonds. The molecular structure of a glycan can be encoded into a direct acyclic graph where each node represents a building block and each edge serves as a chemical linkage between two building blocks. In this context, Graph databases are possible software solutions for storing glycan structures and Graph query languages, such as SPARQL and Cypher, can be used to perform a substructure search. Glycan substructure searching is an important feature for querying structure and experimental glycan databases and retrieving biologically meaningful data. This applies for example to identifying a region of the glycan recognised by a glycan binding protein (GBP). In this study, 19,404 glycan structures were selected from GlycomeDB (www.glycome-db.org) and modelled for being stored into a RDF triple store and a Property Graph. We then performed two different sets of searches and compared the query response times and the results from both technologies to assess performance and accuracy. The two implementations produced the same results, but interestingly we noted a difference in the query response times. Qualitative measures such as portability were also used to define further criteria for choosing the technology adapted to solving glycan substructure search and other comparable issues. Twit Text FOAVip Property Graph vs RDF Triple Store: A Comparison on Glycan Substructure Search ????PLoS One http://www.kidney.de/pget.php?&tw=1&pmid=26656740 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=26656740 #FOAvip English Wikipedia <ref>{{Cite pmid|26656740}}</ref> Property Graph vs RDF Triple Store: A Comparison on Glycan Substructure Search #MMPMID26656740 Alocci D; Mariethoz J; Horlacher O; Bolleman JT; Campbell MP; Lisacek F PLoS One 2015[]; 10 (12): ä PMID26656740show ga Resource description framework (RDF) and Property Graph databases are emerging technologies that are used for storing graph-structured data. We compare these technologies through a molecular biology use case: glycan substructure search. Glycans are branched tree-like molecules composed of building blocks linked together by chemical bonds. The molecular structure of a glycan can be encoded into a direct acyclic graph where each node represents a building block and each edge serves as a chemical linkage between two building blocks. In this context, Graph databases are possible software solutions for storing glycan structures and Graph query languages, such as SPARQL and Cypher, can be used to perform a substructure search. Glycan substructure searching is an important feature for querying structure and experimental glycan databases and retrieving biologically meaningful data. This applies for example to identifying a region of the glycan recognised by a glycan binding protein (GBP). In this study, 19,404 glycan structures were selected from GlycomeDB (www.glycome-db.org) and modelled for being stored into a RDF triple store and a Property Graph. We then performed two different sets of searches and compared the query response times and the results from both technologies to assess performance and accuracy. The two implementations produced the same results, but interestingly we noted a difference in the query response times. Qualitative measures such as portability were also used to define further criteria for choosing the technology adapted to solving glycan substructure search and other comparable issues. äProperty Graph vs RDF Triple Store: A Comparison on Glycan Substructur(epmc).pdf DeepDyve Pubget Overpricing