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An algebra-based method for inferring gene regulatory networks

Overview of attention for article published in BMC Systems Biology, March 2014
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (64th percentile)
  • Good Attention Score compared to outputs of the same age and source (71st percentile)

Mentioned by

twitter
4 tweeters

Citations

dimensions_citation
24 Dimensions

Readers on

mendeley
71 Mendeley
citeulike
1 CiteULike
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Title
An algebra-based method for inferring gene regulatory networks
Published in
BMC Systems Biology, March 2014
DOI 10.1186/1752-0509-8-37
Pubmed ID
Abstract

The inference of gene regulatory networks (GRNs) from experimental observations is at the heart of systems biology. This includes the inference of both the network topology and its dynamics. While there are many algorithms available to infer the network topology from experimental data, less emphasis has been placed on methods that infer network dynamics. Furthermore, since the network inference problem is typically underdetermined, it is essential to have the option of incorporating into the inference process, prior knowledge about the network, along with an effective description of the search space of dynamic models. Finally, it is also important to have an understanding of how a given inference method is affected by experimental and other noise in the data used.

Twitter Demographics

The data shown below were collected from the profiles of 4 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 71 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 2 3%
United Kingdom 2 3%
Turkey 1 1%
Brazil 1 1%
Switzerland 1 1%
United States 1 1%
Unknown 63 89%

Demographic breakdown

Readers by professional status Count As %
Researcher 20 28%
Student > Ph. D. Student 15 21%
Student > Master 12 17%
Student > Bachelor 6 8%
Professor 6 8%
Other 11 15%
Unknown 1 1%
Readers by discipline Count As %
Agricultural and Biological Sciences 31 44%
Computer Science 12 17%
Biochemistry, Genetics and Molecular Biology 8 11%
Mathematics 5 7%
Engineering 2 3%
Other 8 11%
Unknown 5 7%

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 13 August 2014.
All research outputs
#4,791,508
of 10,255,897 outputs
Outputs from BMC Systems Biology
#328
of 960 outputs
Outputs of similar age
#64,206
of 183,887 outputs
Outputs of similar age from BMC Systems Biology
#11
of 42 outputs
Altmetric has tracked 10,255,897 research outputs across all sources so far. This one has received more attention than most of these and is in the 52nd percentile.
So far Altmetric has tracked 960 research outputs from this source. They receive a mean Attention Score of 3.4. This one has gotten more attention than average, scoring higher than 63% 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 183,887 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 64% of its contemporaries.
We're also able to compare this research output to 42 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 71% of its contemporaries.