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Gene interaction enrichment and network analysis to identify dysregulated pathways and their interactions in complex diseases

Overview of attention for article published in BMC Systems Biology, June 2012
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1 X user
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Citations

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45 Dimensions

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84 Mendeley
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3 CiteULike
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Title
Gene interaction enrichment and network analysis to identify dysregulated pathways and their interactions in complex diseases
Published in
BMC Systems Biology, June 2012
DOI 10.1186/1752-0509-6-65
Pubmed ID
Authors

Yu Liu, Mehmet Koyutürk, Jill S Barnholtz-Sloan, Mark R Chance

Abstract

The molecular behavior of biological systems can be described in terms of three fundamental components: (i) the physical entities, (ii) the interactions among these entities, and (iii) the dynamics of these entities and interactions. The mechanisms that drive complex disease can be productively viewed in the context of the perturbations of these components. One challenge in this regard is to identify the pathways altered in specific diseases. To address this challenge, Gene Set Enrichment Analysis (GSEA) and others have been developed, which focus on alterations of individual properties of the entities (such as gene expression). However, the dynamics of the interactions with respect to disease have been less well studied (i.e., properties of components ii and iii).

X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user 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 84 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 2%
Germany 1 1%
Netherlands 1 1%
New Caledonia 1 1%
Chile 1 1%
United Kingdom 1 1%
Austria 1 1%
Unknown 76 90%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 26 31%
Researcher 18 21%
Professor > Associate Professor 10 12%
Student > Bachelor 7 8%
Student > Master 5 6%
Other 14 17%
Unknown 4 5%
Readers by discipline Count As %
Agricultural and Biological Sciences 29 35%
Biochemistry, Genetics and Molecular Biology 13 15%
Computer Science 12 14%
Medicine and Dentistry 6 7%
Engineering 6 7%
Other 9 11%
Unknown 9 11%
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 27 March 2018.
All research outputs
#14,718,998
of 23,577,654 outputs
Outputs from BMC Systems Biology
#544
of 1,139 outputs
Outputs of similar age
#100,231
of 168,645 outputs
Outputs of similar age from BMC Systems Biology
#22
of 40 outputs
Altmetric has tracked 23,577,654 research outputs across all sources so far. This one is in the 35th percentile – i.e., 35% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,139 research outputs from this source. They receive a mean Attention Score of 3.6. This one is in the 47th percentile – i.e., 47% 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 168,645 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 38th percentile – i.e., 38% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 40 others from the same source and published within six weeks on either side of this one. This one is in the 40th percentile – i.e., 40% of its contemporaries scored the same or lower than it.