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Reconstruction of large-scale regulatory networks based on perturbation graphs and transitive reduction: improved methods and their evaluation

Overview of attention for article published in BMC Systems Biology, August 2013
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  • Good Attention Score compared to outputs of the same age and source (66th percentile)

Mentioned by

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2 X users

Citations

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11 Dimensions

Readers on

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77 Mendeley
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3 CiteULike
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Title
Reconstruction of large-scale regulatory networks based on perturbation graphs and transitive reduction: improved methods and their evaluation
Published in
BMC Systems Biology, August 2013
DOI 10.1186/1752-0509-7-73
Pubmed ID
Authors

Andrea Pinna, Sandra Heise, Robert J Flassig, Alberto de la Fuente, Steffen Klamt

Abstract

The data-driven inference of intracellular networks is one of the key challenges of computational and systems biology. As suggested by recent works, a simple yet effective approach for reconstructing regulatory networks comprises the following two steps. First, the observed effects induced by directed perturbations are collected in a signed and directed perturbation graph (PG). In a second step, Transitive Reduction (TR) is used to identify and eliminate those edges in the PG that can be explained by paths and are therefore likely to reflect indirect effects.

X Demographics

X Demographics

The data shown below were collected from the profiles of 2 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 77 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 4 5%
France 1 1%
Brazil 1 1%
Germany 1 1%
Belgium 1 1%
Singapore 1 1%
Japan 1 1%
Denmark 1 1%
Unknown 66 86%

Demographic breakdown

Readers by professional status Count As %
Researcher 29 38%
Student > Ph. D. Student 20 26%
Professor 5 6%
Student > Master 5 6%
Student > Bachelor 4 5%
Other 8 10%
Unknown 6 8%
Readers by discipline Count As %
Agricultural and Biological Sciences 35 45%
Computer Science 13 17%
Biochemistry, Genetics and Molecular Biology 5 6%
Engineering 2 3%
Physics and Astronomy 2 3%
Other 10 13%
Unknown 10 13%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 05 March 2014.
All research outputs
#14,172,739
of 22,715,151 outputs
Outputs from BMC Systems Biology
#544
of 1,142 outputs
Outputs of similar age
#111,020
of 197,294 outputs
Outputs of similar age from BMC Systems Biology
#8
of 24 outputs
Altmetric has tracked 22,715,151 research outputs across all sources so far. This one is in the 35th percentile – i.e., 35% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,142 research outputs from this source. They receive a mean Attention Score of 3.6. This one is in the 47th percentile – i.e., 47% 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 197,294 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 41st percentile – i.e., 41% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 24 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 66% of its contemporaries.