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The human disease network in terms of dysfunctional regulatory mechanisms

Overview of attention for article published in Biology Direct, October 2015
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Title
The human disease network in terms of dysfunctional regulatory mechanisms
Published in
Biology Direct, October 2015
DOI 10.1186/s13062-015-0088-z
Pubmed ID
Authors

Jing Yang, Su-Juan Wu, Wen-Tao Dai, Yi-Xue Li, Yuan-Yuan Li

Abstract

Elucidation of human disease similarities has emerged as an active research area, which is highly relevant to etiology, disease classification, and drug repositioning. In pioneer studies, disease similarity was commonly estimated according to clinical manifestation. Subsequently, scientists started to investigate disease similarity based on gene-phenotype knowledge, which were inevitably biased to well-studied diseases. In recent years, estimating disease similarity according to transcriptomic behavior significantly enhances the probability of finding novel disease relationships, while the currently available studies usually mine expression data through differential expression analysis that has been considered to have little chance of unraveling dysfunctional regulatory relationships, the causal pathogenesis of diseases. We developed a computational approach to measure human disease similarity based on expression data. Differential coexpression analysis, instead of differential expression analysis, was employed to calculate differential coexpression level of every gene for each disease, which was then summarized to the pathway level. Disease similarity was eventually calculated as the partial correlation coefficients of pathways' differential coexpression values between any two diseases. The significance of disease relationships were evaluated by permutation test. Based on mRNA expression data and a differential coexpression analysis based method, we built a human disease network involving 1326 significant Disease-Disease links among 108 diseases. Compared with disease relationships captured by differential expression analysis based method, our disease links shared known disease genes and drugs more significantly. Some novel disease relationships were discovered, for example, Obesity and cancer, Obesity and Psoriasis, lung adenocarcinoma and S. pneumonia, which had been commonly regarded as unrelated to each other, but recently found to share similar molecular mechanisms. Additionally, it was found that both the type of disease and the type of affected tissue influenced the degree of disease similarity. A sub-network including Allergic asthma, Type 2 diabetes and Chronic kidney disease was extracted to demonstrate the exploration of their common pathogenesis. The present study produces a global view of human diseasome for the first time from the viewpoint of regulation mechanisms, which therefore could provide insightful clues to etiology and pathogenesis, and help to perform drug repositioning and design novel therapeutic interventions. This article was reviewed by Limsoon Wong, Rui Wang-Sattler, and Andrey Rzhetsky.

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Mendeley readers

The data shown below were compiled from readership statistics for 74 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 1%
Hungary 1 1%
China 1 1%
Unknown 71 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 18 24%
Student > Master 11 15%
Student > Bachelor 9 12%
Researcher 8 11%
Student > Doctoral Student 6 8%
Other 9 12%
Unknown 13 18%
Readers by discipline Count As %
Medicine and Dentistry 18 24%
Biochemistry, Genetics and Molecular Biology 10 14%
Computer Science 7 9%
Agricultural and Biological Sciences 6 8%
Psychology 5 7%
Other 14 19%
Unknown 14 19%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 10 October 2015.
All research outputs
#2,932,687
of 6,240,950 outputs
Outputs from Biology Direct
#266
of 522 outputs
Outputs of similar age
#102,679
of 193,073 outputs
Outputs of similar age from Biology Direct
#15
of 19 outputs
Altmetric has tracked 6,240,950 research outputs across all sources so far. This one is in the 29th percentile – i.e., 29% of other outputs scored the same or lower than it.
So far Altmetric has tracked 522 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one is in the 4th percentile – i.e., 4% 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 193,073 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 36th percentile – i.e., 36% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 19 others from the same source and published within six weeks on either side of this one. This one is in the 10th percentile – i.e., 10% of its contemporaries scored the same or lower than it.