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Stochastic Boolean networks: An efficient approach to modeling gene regulatory networks

Overview of attention for article published in BMC Systems Biology, August 2012
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Mentioned by

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2 X users

Citations

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62 Dimensions

Readers on

mendeley
109 Mendeley
citeulike
4 CiteULike
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Title
Stochastic Boolean networks: An efficient approach to modeling gene regulatory networks
Published in
BMC Systems Biology, August 2012
DOI 10.1186/1752-0509-6-113
Pubmed ID
Abstract

Various computational models have been of interest due to their use in the modelling of gene regulatory networks (GRNs). As a logical model, probabilistic Boolean networks (PBNs) consider molecular and genetic noise, so the study of PBNs provides significant insights into the understanding of the dynamics of GRNs. This will ultimately lead to advances in developing therapeutic methods that intervene in the process of disease development and progression. The applications of PBNs, however, are hindered by the complexities involved in the computation of the state transition matrix and the steady-state distribution of a PBN. For a PBN with n genes and N Boolean networks, the complexity to compute the state transition matrix is O(nN22n) or O(nN2n) for a sparse matrix.

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

Geographical breakdown

Country Count As %
United States 3 3%
Portugal 2 2%
France 1 <1%
Belgium 1 <1%
Brazil 1 <1%
Unknown 101 93%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 27 25%
Researcher 23 21%
Student > Master 16 15%
Student > Bachelor 8 7%
Student > Doctoral Student 6 6%
Other 18 17%
Unknown 11 10%
Readers by discipline Count As %
Agricultural and Biological Sciences 38 35%
Computer Science 17 16%
Biochemistry, Genetics and Molecular Biology 12 11%
Physics and Astronomy 7 6%
Engineering 7 6%
Other 14 13%
Unknown 14 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 27 August 2013.
All research outputs
#14,164,012
of 22,699,621 outputs
Outputs from BMC Systems Biology
#544
of 1,142 outputs
Outputs of similar age
#99,550
of 170,183 outputs
Outputs of similar age from BMC Systems Biology
#15
of 28 outputs
Altmetric has tracked 22,699,621 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 170,183 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 39th percentile – i.e., 39% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 28 others from the same source and published within six weeks on either side of this one. This one is in the 42nd percentile – i.e., 42% of its contemporaries scored the same or lower than it.