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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

  • Above-average Attention Score compared to outputs of the same age (61st percentile)
  • Above-average Attention Score compared to outputs of the same age and source (58th percentile)

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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.

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The data shown below were collected from the profiles of 6 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 50 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 50 100%

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 6 12%
Unknown 12 24%
Readers by discipline Count As %
Agricultural and Biological Sciences 8 16%
Biochemistry, Genetics and Molecular Biology 7 14%
Computer Science 7 14%
Mathematics 3 6%
Neuroscience 2 4%
Other 9 18%
Unknown 14 28%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 11 July 2017.
All research outputs
#8,328,179
of 25,543,275 outputs
Outputs from BMC Bioinformatics
#3,068
of 7,717 outputs
Outputs of similar age
#122,005
of 326,454 outputs
Outputs of similar age from BMC Bioinformatics
#46
of 114 outputs
Altmetric has tracked 25,543,275 research outputs across all sources so far. This one has received more attention than most of these and is in the 66th percentile.
So far Altmetric has tracked 7,717 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one has gotten more attention than average, scoring higher than 58% 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 326,454 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 61% of its contemporaries.
We're also able to compare this research output to 114 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 58% of its contemporaries.