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Analyzing the genes related to Alzheimer’s disease via a network and pathway-based approach

Overview of attention for article published in Alzheimer's Research & Therapy, April 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)
  • Above-average Attention Score compared to outputs of the same age and source (58th percentile)

Mentioned by

news
1 news outlet
twitter
7 tweeters

Citations

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73 Dimensions

Readers on

mendeley
148 Mendeley
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Title
Analyzing the genes related to Alzheimer’s disease via a network and pathway-based approach
Published in
Alzheimer's Research & Therapy, April 2017
DOI 10.1186/s13195-017-0252-z
Pubmed ID
Authors

Yan-Shi Hu, Juncai Xin, Ying Hu, Lei Zhang, Ju Wang

Abstract

Our understanding of the molecular mechanisms underlying Alzheimer's disease (AD) remains incomplete. Previous studies have revealed that genetic factors provide a significant contribution to the pathogenesis and development of AD. In the past years, numerous genes implicated in this disease have been identified via genetic association studies on candidate genes or at the genome-wide level. However, in many cases, the roles of these genes and their interactions in AD are still unclear. A comprehensive and systematic analysis focusing on the biological function and interactions of these genes in the context of AD will therefore provide valuable insights to understand the molecular features of the disease. In this study, we collected genes potentially associated with AD by screening publications on genetic association studies deposited in PubMed. The major biological themes linked with these genes were then revealed by function and biochemical pathway enrichment analysis, and the relation between the pathways was explored by pathway crosstalk analysis. Furthermore, the network features of these AD-related genes were analyzed in the context of human interactome and an AD-specific network was inferred using the Steiner minimal tree algorithm. We compiled 430 human genes reported to be associated with AD from 823 publications. Biological theme analysis indicated that the biological processes and biochemical pathways related to neurodevelopment, metabolism, cell growth and/or survival, and immunology were enriched in these genes. Pathway crosstalk analysis then revealed that the significantly enriched pathways could be grouped into three interlinked modules-neuronal and metabolic module, cell growth/survival and neuroendocrine pathway module, and immune response-related module-indicating an AD-specific immune-endocrine-neuronal regulatory network. Furthermore, an AD-specific protein network was inferred and novel genes potentially associated with AD were identified. By means of network and pathway-based methodology, we explored the pathogenetic mechanism underlying AD at a systems biology level. Results from our work could provide valuable clues for understanding the molecular mechanism underlying AD. In addition, the framework proposed in this study could be used to investigate the pathological molecular network and genes relevant to other complex diseases or phenotypes.

Twitter Demographics

The data shown below were collected from the profiles of 7 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 148 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 1 <1%
Unknown 147 99%

Demographic breakdown

Readers by professional status Count As %
Researcher 25 17%
Student > Ph. D. Student 22 15%
Student > Bachelor 15 10%
Student > Master 14 9%
Other 11 7%
Other 24 16%
Unknown 37 25%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 37 25%
Neuroscience 23 16%
Agricultural and Biological Sciences 14 9%
Medicine and Dentistry 6 4%
Computer Science 5 3%
Other 20 14%
Unknown 43 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 13 January 2021.
All research outputs
#2,437,736
of 22,968,808 outputs
Outputs from Alzheimer's Research & Therapy
#563
of 1,238 outputs
Outputs of similar age
#47,732
of 309,813 outputs
Outputs of similar age from Alzheimer's Research & Therapy
#10
of 24 outputs
Altmetric has tracked 22,968,808 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 1,238 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 26.0. This one has gotten more attention than average, scoring higher than 54% 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 309,813 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 24 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 58% of its contemporaries.