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Selecting biologically informative genes in co-expression networks with a centrality score

Overview of attention for article published in Biology Direct, June 2014
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Title
Selecting biologically informative genes in co-expression networks with a centrality score
Published in
Biology Direct, June 2014
DOI 10.1186/1745-6150-9-12
Pubmed ID
Authors

Francisco J Azuaje

Abstract

Measures of node centrality in biological networks are useful to detect genes with critical functional roles. In gene co-expression networks, highly connected genes (i.e., candidate hubs) have been associated with key disease-related pathways. Although different approaches to estimating gene centrality are available, their potential biological relevance in gene co-expression networks deserves further investigation. Moreover, standard measures of gene centrality focus on binary interaction networks, which may not always be suitable in the context of co-expression networks. Here, I also investigate a method that identifies potential biologically meaningful genes based on a weighted connectivity score and indicators of statistical relevance.

X Demographics

X Demographics

The data shown below were collected from the profiles of 2 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 103 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Chile 2 2%
United States 2 2%
Colombia 1 <1%
Australia 1 <1%
Germany 1 <1%
United Kingdom 1 <1%
India 1 <1%
Unknown 94 91%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 36 35%
Researcher 16 16%
Student > Master 12 12%
Student > Bachelor 6 6%
Student > Doctoral Student 4 4%
Other 16 16%
Unknown 13 13%
Readers by discipline Count As %
Agricultural and Biological Sciences 42 41%
Biochemistry, Genetics and Molecular Biology 21 20%
Computer Science 9 9%
Mathematics 4 4%
Medicine and Dentistry 3 3%
Other 8 8%
Unknown 16 16%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 July 2017.
All research outputs
#16,721,717
of 25,374,647 outputs
Outputs from Biology Direct
#369
of 537 outputs
Outputs of similar age
#140,000
of 242,774 outputs
Outputs of similar age from Biology Direct
#5
of 11 outputs
Altmetric has tracked 25,374,647 research outputs across all sources so far. This one is in the 32nd percentile – i.e., 32% of other outputs scored the same or lower than it.
So far Altmetric has tracked 537 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.3. This one is in the 28th percentile – i.e., 28% 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 242,774 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 39th percentile – i.e., 39% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 11 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.