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Gene signature-based mapping of immunological systems and diseases

Overview of attention for article published in BMC Bioinformatics, April 2016
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (55th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (54th percentile)

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6 X users

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Title
Gene signature-based mapping of immunological systems and diseases
Published in
BMC Bioinformatics, April 2016
DOI 10.1186/s12859-016-1012-y
Pubmed ID
Authors

Hong Liu, Jessica Liu, Michelle Toups, Timothy Soos, Christopher Arendt

Abstract

The immune system is multifaceted, structured by diverse components that interconnect using multilayered dynamic cellular processes. Genomic technologies provide a means for investigating, at the molecular level, the adaptations of the immune system in host defense and its dysregulation in pathological conditions. A critical aspect of intersecting and investigating complex datasets is determining how to best integrate genomic data from diverse platforms and heterogeneous sample populations to capture immunological signatures in health and disease. We focus on gene signatures, representing highly enriched genes of immune cell subsets from both diseased and healthy tissues. From these, we construct a series of biomaps that illustrate the molecular linkages between cell subsets from different lineages, the connectivity between different immunological diseases, and the enrichment of cell subset signatures in diseased tissues. Finally, we overlay the downstream genes of drug targets with disease gene signatures to display the potential therapeutic applications for these approaches. An in silico approach has been developed to characterize immune cell subsets and diseases based on the gene signatures that most differentiate them from other biological states. This modular 'biomap' reveals the linkages between different diseases and immune subtypes, and provides evidence for the presence of specific immunocyte subsets in mixed tissues. The over-represented genes in disease signatures of interest can be further investigated for their functions in both host defense and disease.

X Demographics

X Demographics

The data shown below were collected from the profiles of 6 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 46 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Netherlands 1 2%
Unknown 45 98%

Demographic breakdown

Readers by professional status Count As %
Researcher 14 30%
Student > Ph. D. Student 6 13%
Student > Master 4 9%
Student > Doctoral Student 2 4%
Student > Bachelor 2 4%
Other 7 15%
Unknown 11 24%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 10 22%
Medicine and Dentistry 5 11%
Agricultural and Biological Sciences 4 9%
Immunology and Microbiology 3 7%
Computer Science 3 7%
Other 8 17%
Unknown 13 28%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 29 April 2016.
All research outputs
#7,478,822
of 22,862,742 outputs
Outputs from BMC Bioinformatics
#3,026
of 7,295 outputs
Outputs of similar age
#107,118
of 299,111 outputs
Outputs of similar age from BMC Bioinformatics
#43
of 97 outputs
Altmetric has tracked 22,862,742 research outputs across all sources so far. This one is in the 44th percentile – i.e., 44% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,295 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one has gotten more attention than average, scoring higher than 50% 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 299,111 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 55% of its contemporaries.
We're also able to compare this research output to 97 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 54% of its contemporaries.