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Efficient randomization of biological networks while preserving functional characterization of individual nodes

Overview of attention for article published in BMC Bioinformatics, December 2016
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (87th percentile)
  • High Attention Score compared to outputs of the same age and source (87th percentile)

Mentioned by

twitter
21 tweeters

Citations

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10 Dimensions

Readers on

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54 Mendeley
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2 CiteULike
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Title
Efficient randomization of biological networks while preserving functional characterization of individual nodes
Published in
BMC Bioinformatics, December 2016
DOI 10.1186/s12859-016-1402-1
Pubmed ID
Authors

Francesco Iorio, Marti Bernardo-Faura, Andrea Gobbi, Thomas Cokelaer, Giuseppe Jurman, Julio Saez-Rodriguez

Abstract

Networks are popular and powerful tools to describe and model biological processes. Many computational methods have been developed to infer biological networks from literature, high-throughput experiments, and combinations of both. Additionally, a wide range of tools has been developed to map experimental data onto reference biological networks, in order to extract meaningful modules. Many of these methods assess results' significance against null distributions of randomized networks. However, these standard unconstrained randomizations do not preserve the functional characterization of the nodes in the reference networks (i.e. their degrees and connection signs), hence including potential biases in the assessment. Building on our previous work about rewiring bipartite networks, we propose a method for rewiring any type of unweighted networks. In particular we formally demonstrate that the problem of rewiring a signed and directed network preserving its functional connectivity (F-rewiring) reduces to the problem of rewiring two induced bipartite networks. Additionally, we reformulate the lower bound to the iterations' number of the switching-algorithm to make it suitable for the F-rewiring of networks of any size. Finally, we present BiRewire3, an open-source Bioconductor package enabling the F-rewiring of any type of unweighted network. We illustrate its application to a case study about the identification of modules from gene expression data mapped on protein interaction networks, and a second one focused on building logic models from more complex signed-directed reference signaling networks and phosphoproteomic data. BiRewire3 it is freely available at https://www.bioconductor.org/packages/BiRewire/ , and it should have a broad application as it allows an efficient and analytically derived statistical assessment of results from any network biology tool.

Twitter Demographics

The data shown below were collected from the profiles of 21 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 54 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 4%
Germany 1 2%
Unknown 51 94%

Demographic breakdown

Readers by professional status Count As %
Researcher 15 28%
Student > Ph. D. Student 12 22%
Student > Master 5 9%
Other 5 9%
Professor 3 6%
Other 9 17%
Unknown 5 9%
Readers by discipline Count As %
Agricultural and Biological Sciences 18 33%
Biochemistry, Genetics and Molecular Biology 11 20%
Medicine and Dentistry 5 9%
Computer Science 5 9%
Engineering 2 4%
Other 5 9%
Unknown 8 15%

Attention Score in Context

This research output has an Altmetric Attention Score of 13. 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 17 August 2017.
All research outputs
#1,235,691
of 13,455,799 outputs
Outputs from BMC Bioinformatics
#448
of 5,027 outputs
Outputs of similar age
#47,904
of 371,206 outputs
Outputs of similar age from BMC Bioinformatics
#51
of 422 outputs
Altmetric has tracked 13,455,799 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 90th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 5,027 research outputs from this source. They receive a mean Attention Score of 4.9. This one has done particularly well, scoring higher than 91% 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 371,206 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 87% of its contemporaries.
We're also able to compare this research output to 422 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 87% of its contemporaries.