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Using gene expression signatures to identify novel treatment strategies in gulf war illness

Overview of attention for article published in BMC Medical Genomics, July 2015
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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 (82nd percentile)
  • High Attention Score compared to outputs of the same age and source (87th percentile)

Mentioned by

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12 tweeters
facebook
4 Facebook pages

Citations

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23 Dimensions

Readers on

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32 Mendeley
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Title
Using gene expression signatures to identify novel treatment strategies in gulf war illness
Published in
BMC Medical Genomics, July 2015
DOI 10.1186/s12920-015-0111-3
Pubmed ID
Authors

Travis J.A. Craddock, Jeanna M. Harvey, Lubov Nathanson, Zachary M. Barnes, Nancy G. Klimas, Mary Ann Fletcher, Gordon Broderick

Abstract

Gulf War Illness (GWI) is a complex multi-symptom disorder that affects up to one in three veterans of this 1991 conflict and for which no effective treatment has been found. Discovering novel treatment strategies for such a complex chronic illness is extremely expensive, carries a high probability of failure and a lengthy cycle time. Repurposing Food and Drug Administration approved drugs offers a cost-effective solution with a significantly abbreviated timeline. Here, we explore drug re-purposing opportunities in GWI by combining systems biology and bioinformatics techniques with pharmacogenomic information to find overlapping elements in gene expression linking GWI to successfully treated diseases. Gene modules were defined based on cellular function and their activation estimated from the differential expression of each module's constituent genes. These gene modules were then cross-referenced with drug atlas and pharmacogenomic databases to identify agents currently used successfully for treatment in other diseases. To explore the clinical use of these drugs in illnesses similar to GWI we compared gene expression patterns in modules that were significantly expressed in GWI with expression patterns in those same modules in other illnesses. We found 19 functional modules with significantly altered gene expression patterns in GWI. Within these modules, 45 genes were documented drug targets. Illnesses with highly correlated gene expression patterns overlapping considerably with GWI were found in 18 of the disease conditions studied. Brain, muscular and autoimmune disorders composed the bulk of these. Of the associated drugs, immunosuppressants currently used in treating rheumatoid arthritis, and hormone based therapies were identified as the best available candidates for treating GWI symptoms.

Twitter Demographics

The data shown below were collected from the profiles of 12 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 32 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 32 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 9 28%
Student > Ph. D. Student 4 13%
Student > Doctoral Student 3 9%
Professor 3 9%
Student > Bachelor 2 6%
Other 1 3%
Unknown 10 31%
Readers by discipline Count As %
Medicine and Dentistry 6 19%
Agricultural and Biological Sciences 5 16%
Neuroscience 3 9%
Immunology and Microbiology 2 6%
Psychology 1 3%
Other 2 6%
Unknown 13 41%

Attention Score in Context

This research output has an Altmetric Attention Score of 9. 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 June 2018.
All research outputs
#2,991,317
of 19,040,944 outputs
Outputs from BMC Medical Genomics
#143
of 1,013 outputs
Outputs of similar age
#41,547
of 240,094 outputs
Outputs of similar age from BMC Medical Genomics
#2
of 8 outputs
Altmetric has tracked 19,040,944 research outputs across all sources so far. Compared to these this one has done well and is in the 84th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,013 research outputs from this source. They receive a mean Attention Score of 4.6. 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 240,094 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 82% of its contemporaries.
We're also able to compare this research output to 8 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.