↓ Skip to main content

Sample size and statistical power considerations in high-dimensionality data settings: a comparative study of classification algorithms

Overview of attention for article published in BMC Bioinformatics, September 2010
Altmetric Badge

About this Attention Score

  • Average Attention Score compared to outputs of the same age

Mentioned by

twitter
2 tweeters

Citations

dimensions_citation
54 Dimensions

Readers on

mendeley
219 Mendeley
citeulike
7 CiteULike
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Sample size and statistical power considerations in high-dimensionality data settings: a comparative study of classification algorithms
Published in
BMC Bioinformatics, September 2010
DOI 10.1186/1471-2105-11-447
Pubmed ID
Authors

Yu Guo, Armin Graber, Robert N McBurney, Raji Balasubramanian

Twitter Demographics

The data shown below were collected from the profiles of 2 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 219 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%
Austria 1 <1%
Australia 1 <1%
Brazil 1 <1%
Germany 1 <1%
South Africa 1 <1%
Malaysia 1 <1%
Sweden 1 <1%
Other 1 <1%
Unknown 200 91%

Demographic breakdown

Readers by professional status Count As %
Researcher 54 25%
Student > Ph. D. Student 43 20%
Student > Master 29 13%
Professor > Associate Professor 19 9%
Professor 17 8%
Other 38 17%
Unknown 19 9%
Readers by discipline Count As %
Agricultural and Biological Sciences 57 26%
Computer Science 23 11%
Medicine and Dentistry 23 11%
Engineering 19 9%
Biochemistry, Genetics and Molecular Biology 18 8%
Other 46 21%
Unknown 33 15%

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 28 March 2022.
All research outputs
#13,072,035
of 20,991,427 outputs
Outputs from BMC Bioinformatics
#4,445
of 6,857 outputs
Outputs of similar age
#219,389
of 395,068 outputs
Outputs of similar age from BMC Bioinformatics
#317
of 484 outputs
Altmetric has tracked 20,991,427 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 6,857 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. 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 395,068 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 41st percentile – i.e., 41% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 484 others from the same source and published within six weeks on either side of this one. This one is in the 29th percentile – i.e., 29% of its contemporaries scored the same or lower than it.