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Granger causality vs. dynamic Bayesian network inference: a comparative study

Overview of attention for article published in BMC Bioinformatics, April 2009
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Title
Granger causality vs. dynamic Bayesian network inference: a comparative study
Published in
BMC Bioinformatics, April 2009
DOI 10.1186/1471-2105-10-122
Pubmed ID
Authors

Cunlu Zou, Jianfeng Feng

Abstract

In computational biology, one often faces the problem of deriving the causal relationship among different elements such as genes, proteins, metabolites, neurons and so on, based upon multi-dimensional temporal data. Currently, there are two common approaches used to explore the network structure among elements. One is the Granger causality approach, and the other is the dynamic Bayesian network inference approach. Both have at least a few thousand publications reported in the literature. A key issue is to choose which approach is used to tackle the data, in particular when they give rise to contradictory results.

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

Geographical breakdown

Country Count As %
United States 7 3%
United Kingdom 4 2%
Switzerland 3 1%
Canada 3 1%
China 2 <1%
Germany 2 <1%
Cuba 1 <1%
Austria 1 <1%
Chile 1 <1%
Other 7 3%
Unknown 200 87%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 81 35%
Researcher 41 18%
Professor > Associate Professor 29 13%
Student > Master 23 10%
Student > Doctoral Student 11 5%
Other 35 15%
Unknown 11 5%
Readers by discipline Count As %
Computer Science 51 22%
Agricultural and Biological Sciences 44 19%
Engineering 41 18%
Mathematics 12 5%
Physics and Astronomy 12 5%
Other 55 24%
Unknown 16 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 06 January 2014.
All research outputs
#15,632,226
of 24,762,960 outputs
Outputs from BMC Bioinformatics
#4,922
of 7,581 outputs
Outputs of similar age
#84,310
of 100,365 outputs
Outputs of similar age from BMC Bioinformatics
#24
of 32 outputs
Altmetric has tracked 24,762,960 research outputs across all sources so far. This one is in the 34th percentile – i.e., 34% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,581 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one is in the 31st percentile – i.e., 31% of its peers scored the same or lower than it.
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We're also able to compare this research output to 32 others from the same source and published within six weeks on either side of this one. This one is in the 25th percentile – i.e., 25% of its contemporaries scored the same or lower than it.