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Attention Score in Context
Title |
Network enrichment analysis: extension of gene-set enrichment analysis to gene networks
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Published in |
BMC Bioinformatics, September 2012
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DOI | 10.1186/1471-2105-13-226 |
Pubmed ID | |
Authors |
Andrey Alexeyenko, Woojoo Lee, Maria Pernemalm, Justin Guegan, Philippe Dessen, Vladimir Lazar, Janne Lehtiö, Yudi Pawitan |
Abstract |
Gene-set enrichment analyses (GEA or GSEA) are commonly used for biological characterization of an experimental gene-set. This is done by finding known functional categories, such as pathways or Gene Ontology terms, that are over-represented in the experimental set; the assessment is based on an overlap statistic. Rich biological information in terms of gene interaction network is now widely available, but this topological information is not used by GEA, so there is a need for methods that exploit this type of information in high-throughput data analysis. |
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 % |
---|---|---|
Spain | 1 | 50% |
Unknown | 1 | 50% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Scientists | 2 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 245 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 8 | 3% |
France | 3 | 1% |
Germany | 2 | <1% |
United Kingdom | 2 | <1% |
Spain | 2 | <1% |
Sweden | 2 | <1% |
Brazil | 1 | <1% |
Australia | 1 | <1% |
Korea, Republic of | 1 | <1% |
Other | 4 | 2% |
Unknown | 219 | 89% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 71 | 29% |
Student > Ph. D. Student | 61 | 25% |
Student > Master | 24 | 10% |
Student > Bachelor | 12 | 5% |
Professor > Associate Professor | 11 | 4% |
Other | 39 | 16% |
Unknown | 27 | 11% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 90 | 37% |
Biochemistry, Genetics and Molecular Biology | 54 | 22% |
Computer Science | 26 | 11% |
Medicine and Dentistry | 10 | 4% |
Mathematics | 10 | 4% |
Other | 24 | 10% |
Unknown | 31 | 13% |
Attention Score in Context
This research output has an Altmetric Attention Score of 4. 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 14 October 2020.
All research outputs
#6,381,374
of 22,678,224 outputs
Outputs from BMC Bioinformatics
#2,468
of 7,249 outputs
Outputs of similar age
#45,900
of 168,561 outputs
Outputs of similar age from BMC Bioinformatics
#27
of 93 outputs
Altmetric has tracked 22,678,224 research outputs across all sources so far. This one has received more attention than most of these and is in the 70th percentile.
So far Altmetric has tracked 7,249 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one has gotten more attention than average, scoring higher than 64% 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 168,561 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 71% of its contemporaries.
We're also able to compare this research output to 93 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 67% of its contemporaries.