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Inferring cellular networks – a review

Overview of attention for article published in BMC Bioinformatics, September 2007
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2 X users

Citations

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340 Dimensions

Readers on

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613 Mendeley
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28 CiteULike
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8 Connotea
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Title
Inferring cellular networks – a review
Published in
BMC Bioinformatics, September 2007
DOI 10.1186/1471-2105-8-s6-s5
Pubmed ID
Authors

Florian Markowetz, Rainer Spang

Abstract

In this review we give an overview of computational and statistical methods to reconstruct cellular networks. Although this area of research is vast and fast developing, we show that most currently used methods can be organized by a few key concepts. The first part of the review deals with conditional independence models including Gaussian graphical models and Bayesian networks. The second part discusses probabilistic and graph-based methods for data from experimental interventions and perturbations.

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 613 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 19 3%
United States 18 3%
Germany 12 2%
France 6 <1%
Spain 4 <1%
Australia 3 <1%
Italy 3 <1%
Sweden 2 <1%
Netherlands 2 <1%
Other 25 4%
Unknown 519 85%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 204 33%
Researcher 147 24%
Student > Master 62 10%
Professor > Associate Professor 43 7%
Professor 34 6%
Other 90 15%
Unknown 33 5%
Readers by discipline Count As %
Agricultural and Biological Sciences 230 38%
Computer Science 105 17%
Biochemistry, Genetics and Molecular Biology 81 13%
Mathematics 43 7%
Engineering 36 6%
Other 75 12%
Unknown 43 7%
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 07 February 2020.
All research outputs
#14,836,083
of 22,846,662 outputs
Outputs from BMC Bioinformatics
#5,047
of 7,291 outputs
Outputs of similar age
#60,722
of 71,443 outputs
Outputs of similar age from BMC Bioinformatics
#38
of 52 outputs
Altmetric has tracked 22,846,662 research outputs across all sources so far. This one is in the 33rd percentile – i.e., 33% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,291 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 26th percentile – i.e., 26% 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 71,443 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 14th percentile – i.e., 14% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 52 others from the same source and published within six weeks on either side of this one. This one is in the 26th percentile – i.e., 26% of its contemporaries scored the same or lower than it.