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Unsupervised gene expression analyses identify IPF-severity correlated signatures, associated genes and biomarkers

Overview of attention for article published in BMC Pulmonary Medicine, October 2017
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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 (85th percentile)

Mentioned by

blogs
1 blog
twitter
10 tweeters

Citations

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24 Dimensions

Readers on

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42 Mendeley
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Title
Unsupervised gene expression analyses identify IPF-severity correlated signatures, associated genes and biomarkers
Published in
BMC Pulmonary Medicine, October 2017
DOI 10.1186/s12890-017-0472-9
Pubmed ID
Authors

Yunguan Wang, Jaswanth Yella, Jing Chen, Francis X. McCormack, Satish K. Madala, Anil G. Jegga

Abstract

Idiopathic Pulmonary Fibrosis (IPF) is a fatal fibrotic lung disease occurring predominantly in middle-aged and older adults. The traditional diagnostic classification of IPF is based on clinical, radiological, and histopathological features. However, the considerable heterogeneity in IPF presentation suggests that differences in gene expression profiles can help to characterize and distinguish disease severity. We used data-driven unsupervised clustering analysis, combined with a knowledge-based approach to identify and characterize IPF subgroups. Using transcriptional profiles on lung tissue from 131 patients with IPF/UIP and 12 non-diseased controls, we identified six subgroups of IPF that generally correlated with the disease severity and lung function decline. Network-informed clustering identified the most severe subgroup of IPF that was enriched with genes regulating inflammatory processes, blood pressure and branching morphogenesis of the lung. The differentially expressed genes in six subgroups of IPF compared to healthy control include transcripts of extracellular matrix, epithelial-mesenchymal cell cross-talk, calcium ion homeostasis, and oxygen transport. Further, we compiled differentially expressed gene signatures to identify unique gene clusters that can segregate IPF from normal, and severe from mild IPF. Additional validations of these signatures were carried out in three independent cohorts of IPF/UIP. Finally, using knowledge-based approaches, we identified several novel candidate genes which may also serve as potential biomarkers of IPF. Discovery of unique and redundant gene signatures for subgroups in IPF can be greatly facilitated through unsupervised clustering. Findings derived from such gene signatures may provide insights into pathogenesis of IPF and facilitate the development of clinically useful biomarkers.

Twitter Demographics

The data shown below were collected from the profiles of 10 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 42 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 42 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 9 21%
Researcher 6 14%
Student > Master 4 10%
Other 3 7%
Student > Bachelor 2 5%
Other 6 14%
Unknown 12 29%
Readers by discipline Count As %
Medicine and Dentistry 12 29%
Biochemistry, Genetics and Molecular Biology 10 24%
Agricultural and Biological Sciences 2 5%
Computer Science 2 5%
Engineering 2 5%
Other 2 5%
Unknown 12 29%

Attention Score in Context

This research output has an Altmetric Attention Score of 13. 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 June 2018.
All research outputs
#2,463,037
of 23,006,268 outputs
Outputs from BMC Pulmonary Medicine
#148
of 1,949 outputs
Outputs of similar age
#50,601
of 328,577 outputs
Outputs of similar age from BMC Pulmonary Medicine
#7
of 48 outputs
Altmetric has tracked 23,006,268 research outputs across all sources so far. Compared to these this one has done well and is in the 89th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,949 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 done particularly well, scoring higher than 92% 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 328,577 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 48 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 85% of its contemporaries.