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EnzML: multi-label prediction of enzyme classes using InterPro signatures

Overview of attention for article published in BMC Bioinformatics, April 2012
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Mentioned by

twitter
3 tweeters

Readers on

mendeley
89 Mendeley
citeulike
4 CiteULike
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Title
EnzML: multi-label prediction of enzyme classes using InterPro signatures
Published in
BMC Bioinformatics, April 2012
DOI 10.1186/1471-2105-13-61
Pubmed ID
Authors

Luna De Ferrari, Stuart Aitken, Jano van Hemert, Igor Goryanin

Abstract

Manual annotation of enzymatic functions cannot keep up with automatic genome sequencing. In this work we explore the capacity of InterPro sequence signatures to automatically predict enzymatic function.

Twitter Demographics

The data shown below were collected from the profiles of 3 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 89 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 2 2%
United States 2 2%
Brazil 1 1%
India 1 1%
Mexico 1 1%
Norway 1 1%
Spain 1 1%
Thailand 1 1%
Unknown 79 89%

Demographic breakdown

Readers by professional status Count As %
Researcher 18 20%
Student > Ph. D. Student 13 15%
Student > Master 12 13%
Student > Bachelor 9 10%
Professor > Associate Professor 8 9%
Other 22 25%
Unknown 7 8%
Readers by discipline Count As %
Agricultural and Biological Sciences 31 35%
Computer Science 20 22%
Biochemistry, Genetics and Molecular Biology 12 13%
Engineering 5 6%
Medicine and Dentistry 5 6%
Other 5 6%
Unknown 11 12%

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 17 May 2013.
All research outputs
#13,066,165
of 21,338,376 outputs
Outputs from BMC Bioinformatics
#4,270
of 6,922 outputs
Outputs of similar age
#85,149
of 151,753 outputs
Outputs of similar age from BMC Bioinformatics
#16
of 24 outputs
Altmetric has tracked 21,338,376 research outputs across all sources so far. This one is in the 37th percentile – i.e., 37% of other outputs scored the same or lower than it.
So far Altmetric has tracked 6,922 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 35th percentile – i.e., 35% 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 151,753 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 43rd percentile – i.e., 43% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 24 others from the same source and published within six weeks on either side of this one. This one is in the 37th percentile – i.e., 37% of its contemporaries scored the same or lower than it.