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A gene pathway analysis highlights the role of cellular adhesion molecules in multiple sclerosis susceptibility

Overview of attention for article published in Genes & Immunity, January 2014
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (84th percentile)
  • High Attention Score compared to outputs of the same age and source (90th percentile)

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blogs
1 blog
twitter
1 X user

Readers on

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86 Mendeley
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2 CiteULike
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Article details
Title
A gene pathway analysis highlights the role of cellular adhesion molecules in multiple sclerosis susceptibility
Published in
Genes & Immunity, January 2014
DOI 10.1038/gene.2013.70
Pubmed ID
Authors
Abstract

Genome-wide association studies (GWASs) perform per-SNP association tests to identify variants involved in disease or trait susceptibility. However, such an approach is not powerful enough to unravel genes that are not individually contributing to the disease/trait, but that may have a role in interaction with other genes as a group. Pathway analysis is an alternative way to highlight such group of genes. Using SNP association P-values from eight multiple sclerosis (MS) GWAS data sets, we performed a candidate pathway analysis for MS susceptibility by considering genes interacting in the cell adhesion molecule (CAMs) biological pathway using Cytoscape software. This network is a strong candidate, as it is involved in the crossing of the blood-brain barrier by the T cells, an early event in MS pathophysiology, and is used as an efficient therapeutic target. We drew up a list of 76 genes belonging to the CAM network. We highlighted 64 networks enriched with CAM genes with low P-values. Filtering by a percentage of CAM genes up to 50% and rejecting enriched signals mainly driven by transcription factors, we highlighted five networks associated with MS susceptibility. One of them, constituted of ITGAL, ICAM1 and ICAM3 genes, could be of interest to develop novel therapeutic targets.

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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 demographics

Mendeley demographics

The data shown below were compiled from readership statistics for 86 Mendeley readers of this research output. Click here to see the associated Mendeley record.
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Geographical breakdown

Geographical breakdown
Country Count As %
United Kingdom 2 2%
Netherlands 1 1%
Korea, Republic of 1 1%
Finland 1 1%
Germany 1 1%
Unknown 80 93%

Demographic breakdown

Readers by professional status
Readers by professional status Count As %
Researcher 20 23%
Student > Ph. D. Student 18 21%
Student > Master 9 10%
Student > Bachelor 6 7%
Professor 5 6%
Other 15 17%
Unknown 13 15%
Readers by discipline
Readers by discipline Count As %
Agricultural and Biological Sciences 20 23%
Medicine and Dentistry 20 23%
Biochemistry, Genetics and Molecular Biology 9 10%
Neuroscience 6 7%
Immunology and Microbiology 5 6%
Other 8 9%
Unknown 18 21%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 8. 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 20 November 2014.
All research outputs
#4,917,020
of 28,840,878 outputs
Outputs from Genes & Immunity
#206
of 893 outputs
Outputs of similar age
#50,528
of 332,719 outputs
Outputs of similar age from Genes & Immunity
#1
of 11 outputs
Altmetric has tracked 28,840,878 research outputs across all sources so far. Compared to these this one has done well and is in the 81st percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 893 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 76% 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 332,719 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 84% of its contemporaries.
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 has done particularly well, scoring higher than 90% of its contemporaries.