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Attention Score in Context
Title |
Granger causality vs. dynamic Bayesian network inference: a comparative study
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Published in |
BMC Bioinformatics, April 2009
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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
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.
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 1 | 50% |
United Kingdom | 1 | 50% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 2 | 100% |
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
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.
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 100,365 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 15th percentile – i.e., 15% of its contemporaries scored the same or lower than it.
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.