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eHive: An Artificial Intelligence workflow system for genomic analysis

Overview of attention for article published in BMC Bioinformatics, January 2010
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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 (93rd percentile)
  • High Attention Score compared to outputs of the same age and source (92nd percentile)

Mentioned by

blogs
1 blog
twitter
5 tweeters
q&a
2 Q&A threads

Citations

dimensions_citation
33 Dimensions

Readers on

mendeley
94 Mendeley
citeulike
15 CiteULike
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Title
eHive: An Artificial Intelligence workflow system for genomic analysis
Published in
BMC Bioinformatics, January 2010
DOI 10.1186/1471-2105-11-240
Pubmed ID
Authors

Jessica Severin, Kathryn Beal, Albert J Vilella, Stephen Fitzgerald, Michael Schuster, Leo Gordon, Abel Ureta-Vidal, Paul Flicek, Javier Herrero

Abstract

The Ensembl project produces updates to its comparative genomics resources with each of its several releases per year. During each release cycle approximately two weeks are allocated to generate all the genomic alignments and the protein homology predictions. The number of calculations required for this task grows approximately quadratically with the number of species. We currently support 50 species in Ensembl and we expect the number to continue to grow in the future.

Twitter Demographics

The data shown below were collected from the profiles of 5 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 94 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 4 4%
United Kingdom 4 4%
Brazil 2 2%
Spain 2 2%
Canada 1 1%
Australia 1 1%
Belgium 1 1%
Sweden 1 1%
Netherlands 1 1%
Other 0 0%
Unknown 77 82%

Demographic breakdown

Readers by professional status Count As %
Researcher 31 33%
Student > Bachelor 13 14%
Student > Ph. D. Student 13 14%
Other 9 10%
Student > Master 8 9%
Other 19 20%
Unknown 1 1%
Readers by discipline Count As %
Agricultural and Biological Sciences 53 56%
Computer Science 15 16%
Biochemistry, Genetics and Molecular Biology 8 9%
Medicine and Dentistry 6 6%
Engineering 3 3%
Other 6 6%
Unknown 3 3%

Attention Score in Context

This research output has an Altmetric Attention Score of 17. 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 23 March 2017.
All research outputs
#787,542
of 12,373,386 outputs
Outputs from BMC Bioinformatics
#237
of 4,576 outputs
Outputs of similar age
#6,009
of 92,118 outputs
Outputs of similar age from BMC Bioinformatics
#3
of 42 outputs
Altmetric has tracked 12,373,386 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 93rd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 4,576 research outputs from this source. They receive a mean Attention Score of 4.9. This one has done particularly well, scoring higher than 94% 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 92,118 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 93% of its contemporaries.
We're also able to compare this research output to 42 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 92% of its contemporaries.