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Systematic analysis of the molecular mechanism underlying atherosclerosis using a text mining approach

Overview of attention for article published in Human Genomics, June 2016
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2 X users

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Title
Systematic analysis of the molecular mechanism underlying atherosclerosis using a text mining approach
Published in
Human Genomics, June 2016
DOI 10.1186/s40246-016-0075-1
Pubmed ID
Authors

Dan Xi, Jinzhen Zhao, Wenyan Lai, Zhigang Guo

Abstract

Atherosclerosis is one of the common health threats all over the world. It is a complex heritable disease that affects arterial blood vessels. Chronic inflammatory response plays an important role in atherogenesis. There has been little success in fully identifying functionally important genes in the pathogenesis of atherosclerosis. In the present study, we performed a systematic analysis of atherosclerosis-related genes using text mining. We identified a total of 1312 genes. Gene ontology (GO) analysis revealed that a total of 35 terms exhibited significance (p < 0.05) as overrepresented terms, indicating that atherosclerosis invokes many genes with a wide range of different functions. Pathway analysis demonstrated that the most highly enriched pathway is the Toll-like receptor signaling pathway. Finally, through gene network analysis, we prioritized 48 genes using the hub gene method. Our study provides a valuable resource for the in-depth understanding of the mechanism underlying atherosclerosis.

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X Demographics

The data shown below were collected from the profiles of 2 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 20 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 20 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 6 30%
Student > Ph. D. Student 2 10%
Lecturer > Senior Lecturer 1 5%
Researcher 1 5%
Student > Postgraduate 1 5%
Other 0 0%
Unknown 9 45%
Readers by discipline Count As %
Medicine and Dentistry 3 15%
Agricultural and Biological Sciences 3 15%
Biochemistry, Genetics and Molecular Biology 2 10%
Pharmacology, Toxicology and Pharmaceutical Science 1 5%
Immunology and Microbiology 1 5%
Other 1 5%
Unknown 9 45%
Attention Score in Context

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 03 June 2016.
All research outputs
#17,286,379
of 25,374,647 outputs
Outputs from Human Genomics
#389
of 564 outputs
Outputs of similar age
#225,409
of 353,818 outputs
Outputs of similar age from Human Genomics
#8
of 11 outputs
Altmetric has tracked 25,374,647 research outputs across all sources so far. This one is in the 21st percentile – i.e., 21% of other outputs scored the same or lower than it.
So far Altmetric has tracked 564 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 7.6. This one is in the 22nd percentile – i.e., 22% 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 353,818 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 28th percentile – i.e., 28% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 11 others from the same source and published within six weeks on either side of this one. This one is in the 18th percentile – i.e., 18% of its contemporaries scored the same or lower than it.