↓ Skip to main content

STON: exploring biological pathways using the SBGN standard and graph databases

Overview of attention for article published in BMC Bioinformatics, December 2016
Altmetric Badge

About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (77th percentile)
  • Good Attention Score compared to outputs of the same age and source (78th percentile)

Mentioned by

twitter
9 tweeters

Citations

dimensions_citation
18 Dimensions

Readers on

mendeley
51 Mendeley
citeulike
1 CiteULike
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
STON: exploring biological pathways using the SBGN standard and graph databases
Published in
BMC Bioinformatics, December 2016
DOI 10.1186/s12859-016-1394-x
Pubmed ID
Authors

Vasundra Touré, Alexander Mazein, Dagmar Waltemath, Irina Balaur, Mansoor Saqi, Ron Henkel, Johann Pellet, Charles Auffray

Abstract

When modeling in Systems Biology and Systems Medicine, the data is often extensive, complex and heterogeneous. Graphs are a natural way of representing biological networks. Graph databases enable efficient storage and processing of the encoded biological relationships. They furthermore support queries on the structure of biological networks. We present the Java-based framework STON (SBGN TO Neo4j). STON imports and translates metabolic, signalling and gene regulatory pathways represented in the Systems Biology Graphical Notation into a graph-oriented format compatible with the Neo4j graph database. STON exploits the power of graph databases to store and query complex biological pathways. This advances the possibility of: i) identifying subnetworks in a given pathway; ii) linking networks across different levels of granularity to address difficulties related to incomplete knowledge representation at single level; and iii) identifying common patterns between pathways in the database.

Twitter Demographics

The data shown below were collected from the profiles of 9 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 51 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 2%
Russia 1 2%
Unknown 49 96%

Demographic breakdown

Readers by professional status Count As %
Researcher 15 29%
Student > Ph. D. Student 11 22%
Student > Bachelor 5 10%
Professor > Associate Professor 4 8%
Student > Master 4 8%
Other 6 12%
Unknown 6 12%
Readers by discipline Count As %
Computer Science 15 29%
Agricultural and Biological Sciences 11 22%
Biochemistry, Genetics and Molecular Biology 6 12%
Engineering 3 6%
Unspecified 1 2%
Other 4 8%
Unknown 11 22%

Attention Score in Context

This research output has an Altmetric Attention Score of 7. 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 06 December 2016.
All research outputs
#4,681,515
of 22,908,162 outputs
Outputs from BMC Bioinformatics
#1,801
of 7,305 outputs
Outputs of similar age
#93,028
of 415,991 outputs
Outputs of similar age from BMC Bioinformatics
#27
of 128 outputs
Altmetric has tracked 22,908,162 research outputs across all sources so far. Compared to these this one has done well and is in the 79th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,305 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one has done well, scoring higher than 75% 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 415,991 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 77% of its contemporaries.
We're also able to compare this research output to 128 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 78% of its contemporaries.