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Network and Pathway-Based Analyses of Genes Associated with Parkinson’s Disease

Overview of attention for article published in Molecular Neurobiology, June 2016
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (85th percentile)
  • High Attention Score compared to outputs of the same age and source (88th percentile)

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1 news outlet
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Citations

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40 Dimensions

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48 Mendeley
Title
Network and Pathway-Based Analyses of Genes Associated with Parkinson’s Disease
Published in
Molecular Neurobiology, June 2016
DOI 10.1007/s12035-016-9998-8
Pubmed ID
Authors

Yanshi Hu, Zhenhua Pan, Ying Hu, Lei Zhang, Ju Wang

Abstract

Parkinson's disease (PD) is a major neurodegenerative disease influenced by both genetic and environmental factors. Although previous studies have provided insights into the significant impacts of genetic factors on PD, the molecular mechanism underlying PD remains largely unclear. Under such situation, a comprehensive analysis focusing on biological function and interactions of PD-related genes will provide us valuable information to understand the pathogenesis of PD. In the current study, by reviewing the literatures deposited in PUBMED, we identified 242 genes genetically associated with PD, referred to as PD-related genes gene set (PDgset). Functional analysis revealed that biological processes and biochemical pathways related to neurodevelopment, metabolism, and immune system were enriched in PDgset. Then, pathway crosstalk analysis indicated that the enriched pathways could be grouped into two modules, with one module consisted of pathways mainly involved in neuronal signaling and another in immune response. Further, based on a global human interactome, we found that PDgset tended to have more moderate degree compared with cancer-related genes. Moreover, PD-specific molecular network was inferred using Steiner minimal tree algorithm and some potential related genes associated with PD were identified. In summary, by using network- and pathway-based methods to explore pathogenetic mechanism underlying PD, results from our work may have important implications for understanding the molecular mechanism underlying PD. Also, the framework proposed in our current work can be used to infer pathological molecular network and genes related to a specific disease.

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X Demographics

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 48 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 2%
Germany 1 2%
Unknown 46 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 9 19%
Researcher 8 17%
Student > Master 5 10%
Student > Postgraduate 4 8%
Student > Doctoral Student 3 6%
Other 4 8%
Unknown 15 31%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 9 19%
Agricultural and Biological Sciences 5 10%
Medicine and Dentistry 5 10%
Neuroscience 4 8%
Immunology and Microbiology 1 2%
Other 7 15%
Unknown 17 35%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 11. 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
#2,713,535
of 22,879,161 outputs
Outputs from Molecular Neurobiology
#352
of 3,467 outputs
Outputs of similar age
#50,165
of 352,119 outputs
Outputs of similar age from Molecular Neurobiology
#10
of 97 outputs
Altmetric has tracked 22,879,161 research outputs across all sources so far. Compared to these this one has done well and is in the 87th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 3,467 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.2. This one has done well, scoring higher than 89% 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 352,119 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 85% of its contemporaries.
We're also able to compare this research output to 97 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 88% of its contemporaries.