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DNetDB: The human disease network database based on dysfunctional regulation mechanism

Overview of attention for article published in BMC Systems Biology, May 2016
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Title
DNetDB: The human disease network database based on dysfunctional regulation mechanism
Published in
BMC Systems Biology, May 2016
DOI 10.1186/s12918-016-0280-5
Pubmed ID
Authors

Jing Yang, Su-Juan Wu, Shao-You Yang, Jia-Wei Peng, Shi-Nuo Wang, Fu-Yan Wang, Yu-Xing Song, Ting Qi, Yi-Xue Li, Yuan-Yuan Li

Abstract

Disease similarity study provides new insights into disease taxonomy, pathogenesis, which plays a guiding role in diagnosis and treatment. The early studies were limited to estimate disease similarities based on clinical manifestations, disease-related genes, medical vocabulary concepts or registry data, which were inevitably biased to well-studied diseases and offered small chance of discovering novel findings in disease relationships. In other words, genome-scale expression data give us another angle to address this problem since simultaneous measurement of the expression of thousands of genes allows for the exploration of gene transcriptional regulation, which is believed to be crucial to biological functions. Although differential expression analysis based methods have the potential to explore new disease relationships, it is difficult to unravel the upstream dysregulation mechanisms of diseases. We therefore estimated disease similarities based on gene expression data by using differential coexpression analysis, a recently emerging method, which has been proved to be more potential to capture dysfunctional regulation mechanisms than differential expression analysis. A total of 1,326 disease relationships among 108 diseases were identified, and the relevant information constituted the human disease network database (DNetDB). Benefiting from the use of differential coexpression analysis, the potential common dysfunctional regulation mechanisms shared by disease pairs (i.e. disease relationships) were extracted and presented. Statistical indicators, common disease-related genes and drugs shared by disease pairs were also included in DNetDB. In total, 1,326 disease relationships among 108 diseases, 5,598 pathways, 7,357 disease-related genes and 342 disease drugs are recorded in DNetDB, among which 3,762 genes and 148 drugs are shared by at least two diseases. DNetDB is the first database focusing on disease similarity from the viewpoint of gene regulation mechanism. It provides an easy-to-use web interface to search and browse the disease relationships and thus helps to systematically investigate etiology and pathogenesis, perform drug repositioning, and design novel therapeutic interventions.Database URL: http://app.scbit.org/DNetDB/ #.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 67 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 2 3%
Brazil 1 1%
Hungary 1 1%
Singapore 1 1%
United States 1 1%
Unknown 61 91%

Demographic breakdown

Readers by professional status Count As %
Researcher 22 33%
Student > Ph. D. Student 8 12%
Student > Bachelor 7 10%
Student > Master 7 10%
Professor 3 4%
Other 8 12%
Unknown 12 18%
Readers by discipline Count As %
Computer Science 15 22%
Biochemistry, Genetics and Molecular Biology 14 21%
Agricultural and Biological Sciences 11 16%
Medicine and Dentistry 4 6%
Engineering 4 6%
Other 7 10%
Unknown 12 18%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 24 May 2016.
All research outputs
#13,980,964
of 22,873,031 outputs
Outputs from BMC Systems Biology
#518
of 1,142 outputs
Outputs of similar age
#181,546
of 333,164 outputs
Outputs of similar age from BMC Systems Biology
#5
of 7 outputs
Altmetric has tracked 22,873,031 research outputs across all sources so far. This one is in the 37th percentile – i.e., 37% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,142 research outputs from this source. They receive a mean Attention Score of 3.6. This one has gotten more attention than average, scoring higher than 52% 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 333,164 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 44th percentile – i.e., 44% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 7 others from the same source and published within six weeks on either side of this one. This one has scored higher than 2 of them.