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mGOASVM: Multi-label protein subcellular localization based on gene ontology and support vector machines

Overview of attention for article published in BMC Bioinformatics, November 2012
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (70th percentile)
  • Good Attention Score compared to outputs of the same age and source (67th percentile)

Mentioned by

twitter
4 tweeters

Citations

dimensions_citation
91 Dimensions

Readers on

mendeley
49 Mendeley
citeulike
1 CiteULike
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Title
mGOASVM: Multi-label protein subcellular localization based on gene ontology and support vector machines
Published in
BMC Bioinformatics, November 2012
DOI 10.1186/1471-2105-13-290
Pubmed ID
Authors

Shibiao Wan, Man-Wai Mak, Sun-Yuan Kung

Abstract

Although many computational methods have been developed to predict protein subcellular localization, most of the methods are limited to the prediction of single-location proteins. Multi-location proteins are either not considered or assumed not existing. However, proteins with multiple locations are particularly interesting because they may have special biological functions, which are essential to both basic research and drug discovery.

Twitter Demographics

The data shown below were collected from the profiles of 4 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 49 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Japan 1 2%
Spain 1 2%
United States 1 2%
Unknown 46 94%

Demographic breakdown

Readers by professional status Count As %
Researcher 14 29%
Student > Master 10 20%
Student > Ph. D. Student 6 12%
Professor > Associate Professor 2 4%
Professor 2 4%
Other 8 16%
Unknown 7 14%
Readers by discipline Count As %
Agricultural and Biological Sciences 12 24%
Computer Science 12 24%
Biochemistry, Genetics and Molecular Biology 7 14%
Unspecified 2 4%
Decision Sciences 1 2%
Other 3 6%
Unknown 12 24%

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 08 November 2012.
All research outputs
#1,647,623
of 4,505,777 outputs
Outputs from BMC Bioinformatics
#1,140
of 2,646 outputs
Outputs of similar age
#23,737
of 81,551 outputs
Outputs of similar age from BMC Bioinformatics
#30
of 92 outputs
Altmetric has tracked 4,505,777 research outputs across all sources so far. This one has received more attention than most of these and is in the 62nd percentile.
So far Altmetric has tracked 2,646 research outputs from this source. They receive a mean Attention Score of 4.2. This one has gotten more attention than average, scoring higher than 53% 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 81,551 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 70% of its contemporaries.
We're also able to compare this research output to 92 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 67% of its contemporaries.