↓ Skip to main content

Sexually-dimorphic targeting of functionally-related genes in COPD

Overview of attention for article published in BMC Systems Biology, November 2014
Altmetric Badge

About this Attention Score

  • Average Attention Score compared to outputs of the same age and source

Mentioned by

twitter
2 X users

Citations

dimensions_citation
50 Dimensions

Readers on

mendeley
39 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
Sexually-dimorphic targeting of functionally-related genes in COPD
Published in
BMC Systems Biology, November 2014
DOI 10.1186/s12918-014-0118-y
Pubmed ID
Authors

Kimberly Glass, John Quackenbush, Edwin K Silverman, Bartolome Celli, Stephen I Rennard, Guo-Cheng Yuan, Dawn L DeMeo

Abstract

BackgroundThere is growing evidence that many diseases develop, progress, and respond to therapy differently in men and women. This variability may manifest as a result of sex-specific structures in gene regulatory networks that influence how those networks operate. However, there are few methods to identify and characterize differences in network structure, slowing progress in understanding mechanisms driving sexual dimorphism.ResultsHere we apply an integrative network inference method, PANDA (Passing Attributes between Networks for Data Assimilation), to model sex-specific networks in blood and sputum samples from subjects with Chronic Obstructive Pulmonary Disease (COPD). We used a jack-knifing approach to build an ensemble of likely networks for each sex. By adapting statistical methods to compare these network ensembles, we were able to identify strong differential-targeting patterns associated with functionally-related sets of genes, including those involved in mitochondrial function and energy metabolism. Network analysis also identified several potential sex- and disease-specific transcriptional regulators of these pathways.ConclusionsNetwork analysis yielded insight into potential mechanisms driving sexual dimorphism in COPD that were not evident from gene expression analysis alone. We believe our ensemble approach to network analysis provides a principled way to capture sex-specific regulatory relationships and could be applied to identify differences in gene regulatory patterns in a wide variety of diseases and contexts.

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 39 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 1 3%
Unknown 38 97%

Demographic breakdown

Readers by professional status Count As %
Researcher 9 23%
Student > Ph. D. Student 5 13%
Student > Master 4 10%
Student > Bachelor 3 8%
Other 3 8%
Other 7 18%
Unknown 8 21%
Readers by discipline Count As %
Agricultural and Biological Sciences 7 18%
Medicine and Dentistry 7 18%
Biochemistry, Genetics and Molecular Biology 4 10%
Mathematics 4 10%
Computer Science 3 8%
Other 5 13%
Unknown 9 23%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 October 2015.
All research outputs
#17,733,724
of 22,772,779 outputs
Outputs from BMC Systems Biology
#770
of 1,142 outputs
Outputs of similar age
#248,101
of 361,884 outputs
Outputs of similar age from BMC Systems Biology
#33
of 54 outputs
Altmetric has tracked 22,772,779 research outputs across all sources so far. This one is in the 19th percentile – i.e., 19% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,142 research outputs from this source. They receive a mean Attention Score of 3.6. This one is in the 27th percentile – i.e., 27% 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 361,884 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 27th percentile – i.e., 27% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 54 others from the same source and published within six weeks on either side of this one. This one is in the 31st percentile – i.e., 31% of its contemporaries scored the same or lower than it.