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A flood-based information flow analysis and network minimization method for gene regulatory networks

Overview of attention for article published in BMC Bioinformatics, April 2013
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Mentioned by

twitter
2 tweeters

Citations

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3 Dimensions

Readers on

mendeley
33 Mendeley
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3 CiteULike
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Title
A flood-based information flow analysis and network minimization method for gene regulatory networks
Published in
BMC Bioinformatics, April 2013
DOI 10.1186/1471-2105-14-137
Pubmed ID
Authors

Andreas Pavlogiannis, Vadim Mozhayskiy, Ilias Tagkopoulos

Abstract

Biological networks tend to have high interconnectivity, complex topologies and multiple types of interactions. This renders difficult the identification of sub-networks that are involved in condition- specific responses. In addition, we generally lack scalable methods that can reveal the information flow in gene regulatory and biochemical pathways. Doing so will help us to identify key participants and paths under specific environmental and cellular context.

Twitter Demographics

The data shown below were collected from the profiles of 2 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 33 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Netherlands 1 3%
Italy 1 3%
Australia 1 3%
Brazil 1 3%
Mexico 1 3%
Unknown 28 85%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 12 36%
Researcher 8 24%
Student > Master 2 6%
Other 2 6%
Professor 2 6%
Other 3 9%
Unknown 4 12%
Readers by discipline Count As %
Agricultural and Biological Sciences 11 33%
Computer Science 9 27%
Environmental Science 2 6%
Biochemistry, Genetics and Molecular Biology 2 6%
Psychology 2 6%
Other 4 12%
Unknown 3 9%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 26 April 2013.
All research outputs
#11,070,003
of 14,573,111 outputs
Outputs from BMC Bioinformatics
#4,225
of 5,420 outputs
Outputs of similar age
#100,310
of 151,319 outputs
Outputs of similar age from BMC Bioinformatics
#18
of 21 outputs
Altmetric has tracked 14,573,111 research outputs across all sources so far. This one is in the 20th percentile – i.e., 20% of other outputs scored the same or lower than it.
So far Altmetric has tracked 5,420 research outputs from this source. They receive a mean Attention Score of 4.9. This one is in the 16th percentile – i.e., 16% of its peers scored the same or lower than it.
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 151,319 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 29th percentile – i.e., 29% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 21 others from the same source and published within six weeks on either side of this one. This one is in the 14th percentile – i.e., 14% of its contemporaries scored the same or lower than it.