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Advanced SPARQL querying in small molecule databases

Overview of attention for article published in Journal of Cheminformatics, June 2016
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (81st percentile)
  • Above-average Attention Score compared to outputs of the same age and source (64th percentile)

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12 X users

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33 Mendeley
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Title
Advanced SPARQL querying in small molecule databases
Published in
Journal of Cheminformatics, June 2016
DOI 10.1186/s13321-016-0144-4
Pubmed ID
Authors

Jakub Galgonek, Tomáš Hurt, Vendula Michlíková, Petr Onderka, Jan Schwarz, Jiří Vondrášek

Abstract

In recent years, the Resource Description Framework (RDF) and the SPARQL query language have become more widely used in the area of cheminformatics and bioinformatics databases. These technologies allow better interoperability of various data sources and powerful searching facilities. However, we identified several deficiencies that make usage of such RDF databases restrictive or challenging for common users. We extended a SPARQL engine to be able to use special procedures inside SPARQL queries. This allows the user to work with data that cannot be simply precomputed and thus cannot be directly stored in the database. We designed an algorithm that checks a query against data ontology to identify possible user errors. This greatly improves query debugging. We also introduced an approach to visualize retrieved data in a user-friendly way, based on templates describing visualizations of resource classes. To integrate all of our approaches, we developed a simple web application. Our system was implemented successfully, and we demonstrated its usability on the ChEBI database transformed into RDF form. To demonstrate procedure call functions, we employed compound similarity searching based on OrChem. The application is publicly available at https://bioinfo.uochb.cas.cz/projects/chemRDF.

X Demographics

X Demographics

The data shown below were collected from the profiles of 12 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 33 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 3%
India 1 3%
Sweden 1 3%
Unknown 30 91%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 8 24%
Student > Master 5 15%
Researcher 4 12%
Student > Doctoral Student 3 9%
Student > Bachelor 2 6%
Other 6 18%
Unknown 5 15%
Readers by discipline Count As %
Computer Science 11 33%
Agricultural and Biological Sciences 6 18%
Chemistry 5 15%
Biochemistry, Genetics and Molecular Biology 3 9%
Pharmacology, Toxicology and Pharmaceutical Science 2 6%
Other 1 3%
Unknown 5 15%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 10. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 03 August 2018.
All research outputs
#3,687,677
of 24,903,209 outputs
Outputs from Journal of Cheminformatics
#344
of 934 outputs
Outputs of similar age
#63,109
of 347,806 outputs
Outputs of similar age from Journal of Cheminformatics
#6
of 14 outputs
Altmetric has tracked 24,903,209 research outputs across all sources so far. Compared to these this one has done well and is in the 85th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 934 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.2. This one has gotten more attention than average, scoring higher than 63% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 347,806 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 81% of its contemporaries.
We're also able to compare this research output to 14 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 64% of its contemporaries.