↓ Skip to main content

Semi-supervised protein subcellular localization

Overview of attention for article published in BMC Bioinformatics, January 2009
Altmetric Badge

About this Attention Score

  • Good Attention Score compared to outputs of the same age (72nd percentile)
  • Above-average Attention Score compared to outputs of the same age and source (64th percentile)

Mentioned by

wikipedia
1 Wikipedia page

Citations

dimensions_citation
22 Dimensions

Readers on

mendeley
20 Mendeley
citeulike
1 CiteULike
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Semi-supervised protein subcellular localization
Published in
BMC Bioinformatics, January 2009
DOI 10.1186/1471-2105-10-s1-s47
Pubmed ID
Authors

Qian Xu, Derek Hao Hu, Hong Xue, Weichuan Yu, Qiang Yang

Abstract

Protein subcellular localization is concerned with predicting the location of a protein within a cell using computational method. The location information can indicate key functionalities of proteins. Accurate predictions of subcellular localizations of protein can aid the prediction of protein function and genome annotation, as well as the identification of drug targets. Computational methods based on machine learning, such as support vector machine approaches, have already been widely used in the prediction of protein subcellular localization. However, a major drawback of these machine learning-based approaches is that a large amount of data should be labeled in order to let the prediction system learn a classifier of good generalization ability. However, in real world cases, it is laborious, expensive and time-consuming to experimentally determine the subcellular localization of a protein and prepare instances of labeled data.

Mendeley readers

The data shown below were compiled from readership statistics for 20 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Malaysia 1 5%
United Kingdom 1 5%
United States 1 5%
Greece 1 5%
Unknown 16 80%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 7 35%
Student > Bachelor 4 20%
Professor > Associate Professor 3 15%
Other 2 10%
Student > Doctoral Student 1 5%
Other 1 5%
Unknown 2 10%
Readers by discipline Count As %
Computer Science 10 50%
Agricultural and Biological Sciences 3 15%
Engineering 2 10%
Biochemistry, Genetics and Molecular Biology 1 5%
Medicine and Dentistry 1 5%
Other 1 5%
Unknown 2 10%

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 03 May 2009.
All research outputs
#816,291
of 3,635,018 outputs
Outputs from BMC Bioinformatics
#785
of 2,289 outputs
Outputs of similar age
#25,236
of 96,325 outputs
Outputs of similar age from BMC Bioinformatics
#45
of 137 outputs
Altmetric has tracked 3,635,018 research outputs across all sources so far. This one has received more attention than most of these and is in the 63rd percentile.
So far Altmetric has tracked 2,289 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 58% 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 96,325 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 72% of its contemporaries.
We're also able to compare this research output to 137 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 64% of its contemporaries.