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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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Citations

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106 Dimensions

Readers on

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53 Mendeley
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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.

X Demographics

X Demographics

The data shown below were collected from the profiles of 4 X users 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 53 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 50 94%

Demographic breakdown

Readers by professional status Count As %
Researcher 14 26%
Student > Master 10 19%
Student > Ph. D. Student 6 11%
Professor > Associate Professor 3 6%
Professor 2 4%
Other 7 13%
Unknown 11 21%
Readers by discipline Count As %
Computer Science 13 25%
Agricultural and Biological Sciences 12 23%
Biochemistry, Genetics and Molecular Biology 7 13%
Decision Sciences 1 2%
Medicine and Dentistry 1 2%
Other 2 4%
Unknown 17 32%
Attention Score in Context

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
#12,864,199
of 22,685,926 outputs
Outputs from BMC Bioinformatics
#3,780
of 7,253 outputs
Outputs of similar age
#95,797
of 183,491 outputs
Outputs of similar age from BMC Bioinformatics
#58
of 111 outputs
Altmetric has tracked 22,685,926 research outputs across all sources so far. This one is in the 42nd percentile – i.e., 42% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,253 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 45th percentile – i.e., 45% 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 183,491 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 47th percentile – i.e., 47% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 111 others from the same source and published within six weeks on either side of this one. This one is in the 45th percentile – i.e., 45% of its contemporaries scored the same or lower than it.