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Hybrid coexpression link similarity graph clustering for mining biological modules from multiple gene expression datasets

Overview of attention for article published in BioData Mining, August 2014
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (84th percentile)
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

blogs
1 blog
twitter
2 tweeters

Citations

dimensions_citation
5 Dimensions

Readers on

mendeley
26 Mendeley
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Title
Hybrid coexpression link similarity graph clustering for mining biological modules from multiple gene expression datasets
Published in
BioData Mining, August 2014
DOI 10.1186/1756-0381-7-16
Pubmed ID
Authors

Saeed Salem, Cagri Ozcaglar

Abstract

Advances in genomic technologies have enabled the accumulation of vast amount of genomic data, including gene expression data for multiple species under various biological and environmental conditions. Integration of these gene expression datasets is a promising strategy to alleviate the challenges of protein functional annotation and biological module discovery based on a single gene expression data, which suffers from spurious coexpression.

Twitter Demographics

The data shown below were collected from the profiles of 2 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 26 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 8%
France 1 4%
Brazil 1 4%
Unknown 22 85%

Demographic breakdown

Readers by professional status Count As %
Researcher 11 42%
Student > Ph. D. Student 7 27%
Student > Postgraduate 2 8%
Student > Master 1 4%
Professor 1 4%
Other 1 4%
Unknown 3 12%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 9 35%
Computer Science 6 23%
Agricultural and Biological Sciences 5 19%
Mathematics 2 8%
Engineering 2 8%
Other 0 0%
Unknown 2 8%

Attention Score in Context

This research output has an Altmetric Attention Score of 8. 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 16 September 2014.
All research outputs
#1,720,221
of 12,076,109 outputs
Outputs from BioData Mining
#70
of 219 outputs
Outputs of similar age
#32,126
of 211,022 outputs
Outputs of similar age from BioData Mining
#3
of 5 outputs
Altmetric has tracked 12,076,109 research outputs across all sources so far. Compared to these this one has done well and is in the 85th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 219 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.2. This one has gotten more attention than average, scoring higher than 68% 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 211,022 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 84% of its contemporaries.
We're also able to compare this research output to 5 others from the same source and published within six weeks on either side of this one. This one has scored higher than 2 of them.