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R/parallel – speeding up bioinformatics analysis with R

Overview of attention for article published in BMC Bioinformatics, September 2008
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (85th percentile)
  • Good Attention Score compared to outputs of the same age and source (79th percentile)

Mentioned by

blogs
1 blog
patent
1 patent

Citations

dimensions_citation
20 Dimensions

Readers on

mendeley
157 Mendeley
citeulike
40 CiteULike
connotea
8 Connotea
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Title
R/parallel – speeding up bioinformatics analysis with R
Published in
BMC Bioinformatics, September 2008
DOI 10.1186/1471-2105-9-390
Pubmed ID
Authors

Gonzalo Vera, Ritsert C Jansen, Remo L Suppi

Abstract

R is the preferred tool for statistical analysis of many bioinformaticians due in part to the increasing number of freely available analytical methods. Such methods can be quickly reused and adapted to each particular experiment. However, in experiments where large amounts of data are generated, for example using high-throughput screening devices, the processing time required to analyze data is often quite long. A solution to reduce the processing time is the use of parallel computing technologies. Because R does not support parallel computations, several tools have been developed to enable such technologies. However, these tools require multiple modications to the way R programs are usually written or run. Although these tools can finally speed up the calculations, the time, skills and additional resources required to use them are an obstacle for most bioinformaticians.

Mendeley readers

The data shown below were compiled from readership statistics for 157 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 7 4%
United Kingdom 6 4%
France 3 2%
Brazil 3 2%
Germany 3 2%
Netherlands 2 1%
Switzerland 1 <1%
Sweden 1 <1%
Colombia 1 <1%
Other 7 4%
Unknown 123 78%

Demographic breakdown

Readers by professional status Count As %
Researcher 58 37%
Student > Ph. D. Student 39 25%
Professor > Associate Professor 13 8%
Student > Master 12 8%
Professor 12 8%
Other 19 12%
Unknown 4 3%
Readers by discipline Count As %
Agricultural and Biological Sciences 88 56%
Computer Science 22 14%
Biochemistry, Genetics and Molecular Biology 11 7%
Environmental Science 6 4%
Mathematics 6 4%
Other 17 11%
Unknown 7 4%

Attention Score in Context

This research output has an Altmetric Attention Score of 9. 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 09 July 2019.
All research outputs
#2,268,931
of 15,398,206 outputs
Outputs from BMC Bioinformatics
#891
of 5,625 outputs
Outputs of similar age
#16,099
of 113,848 outputs
Outputs of similar age from BMC Bioinformatics
#36
of 178 outputs
Altmetric has tracked 15,398,206 research outputs across all sources so far. Compared to these this one has done well and is in the 85th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 5,625 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.0. This one has done well, scoring higher than 84% 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 113,848 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 85% of its contemporaries.
We're also able to compare this research output to 178 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 79% of its contemporaries.