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Differential co-expression network centrality and machine learning feature selection for identifying susceptibility hubs in networks with scale-free structure

Overview of attention for article published in BioData Mining, February 2015
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#35 of 240)
  • High Attention Score compared to outputs of the same age (91st percentile)

Mentioned by

twitter
24 tweeters
facebook
2 Facebook pages
googleplus
1 Google+ user

Citations

dimensions_citation
20 Dimensions

Readers on

mendeley
61 Mendeley
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Title
Differential co-expression network centrality and machine learning feature selection for identifying susceptibility hubs in networks with scale-free structure
Published in
BioData Mining, February 2015
DOI 10.1186/s13040-015-0040-x
Pubmed ID
Authors

Caleb A Lareau, Bill C White, Ann L Oberg, Brett A McKinney

Abstract

Biological insights into group differences, such as disease status, have been achieved through differential co-expression analysis of microarray data. Additional understanding of group differences may be achieved by integrating the connectivity structure of the differential co-expression network and per-gene differential expression between phenotypic groups. Such a global differential co-expression network strategy may increase sensitivity to detect gene-gene interactions (or expression epistasis) that may act as candidates for rewiring susceptibility co-expression networks.

Twitter Demographics

The data shown below were collected from the profiles of 24 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 61 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 2%
Canada 1 2%
Unknown 59 97%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 14 23%
Researcher 14 23%
Student > Master 9 15%
Student > Postgraduate 5 8%
Student > Bachelor 4 7%
Other 10 16%
Unknown 5 8%
Readers by discipline Count As %
Agricultural and Biological Sciences 22 36%
Biochemistry, Genetics and Molecular Biology 17 28%
Computer Science 5 8%
Medicine and Dentistry 3 5%
Psychology 2 3%
Other 4 7%
Unknown 8 13%

Attention Score in Context

This research output has an Altmetric Attention Score of 16. 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 January 2017.
All research outputs
#1,070,663
of 13,978,928 outputs
Outputs from BioData Mining
#35
of 240 outputs
Outputs of similar age
#24,365
of 283,463 outputs
Outputs of similar age from BioData Mining
#1
of 1 outputs
Altmetric has tracked 13,978,928 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 92nd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 240 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.3. This one has done well, scoring higher than 85% 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 283,463 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 91% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them