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Extracting reaction networks from databases–opening Pandora’s box

Overview of attention for article published in Briefings in Bioinformatics, August 2013
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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 (92nd percentile)
  • High Attention Score compared to outputs of the same age and source (92nd percentile)

Mentioned by

blogs
1 blog
twitter
22 X users
facebook
1 Facebook page

Citations

dimensions_citation
5 Dimensions

Readers on

mendeley
72 Mendeley
citeulike
7 CiteULike
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Title
Extracting reaction networks from databases–opening Pandora’s box
Published in
Briefings in Bioinformatics, August 2013
DOI 10.1093/bib/bbt058
Pubmed ID
Authors

Liam G. Fearnley, Melissa J. Davis, Mark A. Ragan, Lars K. Nielsen

Abstract

Large quantities of information describing the mechanisms of biological pathways continue to be collected in publicly available databases. At the same time, experiments have increased in scale, and biologists increasingly use pathways defined in online databases to interpret the results of experiments and generate hypotheses. Emerging computational techniques that exploit the rich biological information captured in reaction systems require formal standardized descriptions of pathways to extract these reaction networks and avoid the alternative: time-consuming and largely manual literature-based network reconstruction. Here, we systematically evaluate the effects of commonly used knowledge representations on the seemingly simple task of extracting a reaction network describing signal transduction from a pathway database. We show that this process is in fact surprisingly difficult, and the pathway representations adopted by various knowledge bases have dramatic consequences for reaction network extraction, connectivity, capture of pathway crosstalk and in the modelling of cell-cell interactions. Researchers constructing computational models built from automatically extracted reaction networks must therefore consider the issues we outline in this review to maximize the value of existing pathway knowledge.

X Demographics

X Demographics

The data shown below were collected from the profiles of 22 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 72 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 4 6%
France 2 3%
Portugal 1 1%
Chile 1 1%
Ghana 1 1%
Germany 1 1%
United Kingdom 1 1%
Brazil 1 1%
Japan 1 1%
Other 1 1%
Unknown 58 81%

Demographic breakdown

Readers by professional status Count As %
Researcher 27 38%
Student > Ph. D. Student 17 24%
Professor 4 6%
Other 4 6%
Student > Master 4 6%
Other 11 15%
Unknown 5 7%
Readers by discipline Count As %
Agricultural and Biological Sciences 27 38%
Biochemistry, Genetics and Molecular Biology 14 19%
Computer Science 9 13%
Engineering 6 8%
Business, Management and Accounting 2 3%
Other 7 10%
Unknown 7 10%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 21. 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 22 January 2015.
All research outputs
#1,568,016
of 23,577,654 outputs
Outputs from Briefings in Bioinformatics
#134
of 2,669 outputs
Outputs of similar age
#13,935
of 198,136 outputs
Outputs of similar age from Briefings in Bioinformatics
#2
of 25 outputs
Altmetric has tracked 23,577,654 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 93rd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 2,669 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.7. This one has done particularly well, scoring higher than 94% 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 198,136 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 92% of its contemporaries.
We're also able to compare this research output to 25 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 92% of its contemporaries.