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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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1 tweeter

Citations

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9 Dimensions

Readers on

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73 Mendeley
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8 CiteULike
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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.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter 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 73 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 64 88%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 23 32%
Researcher 14 19%
Student > Master 8 11%
Student > Postgraduate 5 7%
Student > Doctoral Student 5 7%
Other 16 22%
Unknown 2 3%
Readers by discipline Count As %
Agricultural and Biological Sciences 30 41%
Computer Science 20 27%
Biochemistry, Genetics and Molecular Biology 6 8%
Engineering 6 8%
Mathematics 2 3%
Other 7 10%
Unknown 2 3%

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
#9,906,138
of 12,373,386 outputs
Outputs from BMC Bioinformatics
#3,816
of 4,576 outputs
Outputs of similar age
#66,429
of 86,779 outputs
Outputs of similar age from BMC Bioinformatics
#41
of 48 outputs
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So far Altmetric has tracked 4,576 research outputs from this source. They receive a mean Attention Score of 4.9. This one is in the 7th percentile – i.e., 7% of its peers scored the same or lower than it.
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We're also able to compare this research output to 48 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.