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Inferring functional modules of protein families with probabilistic topic models

Overview of attention for article published in BMC Bioinformatics, May 2011
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Title
Inferring functional modules of protein families with probabilistic topic models
Published in
BMC Bioinformatics, May 2011
DOI 10.1186/1471-2105-12-141
Pubmed ID
Authors

Sebastian GA Konietzny, Laura Dietz, Alice C McHardy

Abstract

Genome and metagenome studies have identified thousands of protein families whose functions are poorly understood and for which techniques for functional characterization provide only partial information. For such proteins, the genome context can give further information about their functional context.

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The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 75 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 4 5%
Japan 2 3%
Sweden 1 1%
Brazil 1 1%
Spain 1 1%
Unknown 66 88%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 23 31%
Researcher 14 19%
Student > Master 8 11%
Student > Doctoral Student 6 8%
Student > Postgraduate 5 7%
Other 16 21%
Unknown 3 4%
Readers by discipline Count As %
Agricultural and Biological Sciences 31 41%
Computer Science 20 27%
Biochemistry, Genetics and Molecular Biology 7 9%
Engineering 5 7%
Mathematics 2 3%
Other 8 11%
Unknown 2 3%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 15 November 2011.
All research outputs
#18,300,116
of 22,656,971 outputs
Outputs from BMC Bioinformatics
#6,276
of 7,236 outputs
Outputs of similar age
#94,840
of 109,811 outputs
Outputs of similar age from BMC Bioinformatics
#66
of 80 outputs
Altmetric has tracked 22,656,971 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,236 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 5th percentile – i.e., 5% 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 109,811 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 6th percentile – i.e., 6% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 80 others from the same source and published within six weeks on either side of this one. This one is in the 5th percentile – i.e., 5% of its contemporaries scored the same or lower than it.