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Mendeley readers
Attention Score in Context
Title |
Differential co-expression network centrality and machine learning feature selection for identifying susceptibility hubs in networks with scale-free structure
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Published in |
BioData Mining, February 2015
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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. |
X Demographics
The data shown below were collected from the profiles of 22 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 8 | 36% |
Belgium | 1 | 5% |
Russia | 1 | 5% |
France | 1 | 5% |
New Zealand | 1 | 5% |
Chile | 1 | 5% |
Italy | 1 | 5% |
United Kingdom | 1 | 5% |
Canada | 1 | 5% |
Other | 0 | 0% |
Unknown | 6 | 27% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 19 | 86% |
Scientists | 2 | 9% |
Science communicators (journalists, bloggers, editors) | 1 | 5% |
Mendeley readers
The data shown below were compiled from readership statistics for 65 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 | 63 | 97% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 16 | 25% |
Researcher | 13 | 20% |
Student > Master | 8 | 12% |
Student > Postgraduate | 6 | 9% |
Student > Bachelor | 4 | 6% |
Other | 11 | 17% |
Unknown | 7 | 11% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 22 | 34% |
Biochemistry, Genetics and Molecular Biology | 15 | 23% |
Computer Science | 7 | 11% |
Medicine and Dentistry | 4 | 6% |
Psychology | 2 | 3% |
Other | 5 | 8% |
Unknown | 10 | 15% |
Attention Score in Context
This research output has an Altmetric Attention Score of 14. 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
#2,219,125
of 22,789,566 outputs
Outputs from BioData Mining
#46
of 307 outputs
Outputs of similar age
#33,856
of 352,356 outputs
Outputs of similar age from BioData Mining
#3
of 9 outputs
Altmetric has tracked 22,789,566 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 90th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 307 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 7.7. 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 352,356 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 90% of its contemporaries.
We're also able to compare this research output to 9 others from the same source and published within six weeks on either side of this one. This one has scored higher than 6 of them.