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sgnesR: An R package for simulating gene expression data from an underlying real gene network structure considering delay parameters

Overview of attention for article published in BMC Bioinformatics, July 2017
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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 (73rd percentile)
  • High Attention Score compared to outputs of the same age and source (83rd percentile)

Mentioned by

twitter
12 tweeters
facebook
1 Facebook page

Citations

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6 Dimensions

Readers on

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49 Mendeley
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Title
sgnesR: An R package for simulating gene expression data from an underlying real gene network structure considering delay parameters
Published in
BMC Bioinformatics, July 2017
DOI 10.1186/s12859-017-1731-8
Pubmed ID
Authors

Shailesh Tripathi, Jason Lloyd-Price, Andre Ribeiro, Olli Yli-Harja, Matthias Dehmer, Frank Emmert-Streib

Abstract

sgnesR (Stochastic Gene Network Expression Simulator in R) is an R package that provides an interface to simulate gene expression data from a given gene network using the stochastic simulation algorithm (SSA). The package allows various options for delay parameters and can easily included in reactions for promoter delay, RNA delay and Protein delay. A user can tune these parameters to model various types of reactions within a cell. As examples, we present two network models to generate expression profiles. We also demonstrated the inference of networks and the evaluation of association measure of edge and non-edge components from the generated expression profiles. The purpose of sgnesR is to enable an easy to use and a quick implementation for generating realistic gene expression data from biologically relevant networks that can be user selected. sgnesR is freely available for academic use. The R package has been tested for R 3.2.0 under Linux, Windows and Mac OS X.

Twitter Demographics

The data shown below were collected from the profiles of 12 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 49 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 49 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 11 22%
Researcher 7 14%
Student > Bachelor 6 12%
Other 5 10%
Student > Master 3 6%
Other 5 10%
Unknown 12 24%
Readers by discipline Count As %
Agricultural and Biological Sciences 9 18%
Biochemistry, Genetics and Molecular Biology 7 14%
Computer Science 6 12%
Mathematics 3 6%
Engineering 2 4%
Other 8 16%
Unknown 14 29%

Attention Score in Context

This research output has an Altmetric Attention Score of 6. 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 14 July 2017.
All research outputs
#3,087,880
of 13,562,740 outputs
Outputs from BMC Bioinformatics
#1,282
of 5,059 outputs
Outputs of similar age
#70,803
of 265,174 outputs
Outputs of similar age from BMC Bioinformatics
#1
of 6 outputs
Altmetric has tracked 13,562,740 research outputs across all sources so far. Compared to these this one has done well and is in the 77th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 5,059 research outputs from this source. They receive a mean Attention Score of 4.8. This one has gotten more attention than average, scoring higher than 74% 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 265,174 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 73% of its contemporaries.
We're also able to compare this research output to 6 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them