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Phenome-based gene discovery provides information about Parkinson’s disease drug targets

Overview of attention for article published in BMC Genomics, August 2016
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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 (85th percentile)
  • High Attention Score compared to outputs of the same age and source (93rd percentile)

Mentioned by

news
1 news outlet
twitter
4 tweeters

Citations

dimensions_citation
11 Dimensions

Readers on

mendeley
19 Mendeley
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Title
Phenome-based gene discovery provides information about Parkinson’s disease drug targets
Published in
BMC Genomics, August 2016
DOI 10.1186/s12864-016-2820-1
Pubmed ID
Authors

Yang Chen, Rong Xu

Abstract

Parkinson disease (PD) is a severe neurodegenerative disease without curative drugs. The highly complex and heterogeneous disease mechanisms are still unclear. Detecting novel PD associated genes not only contributes in revealing the disease pathogenesis, but also facilitates discovering new targets for drugs. We propose a phenome-based gene prediction strategy to identify disease-associated genes for PD. We integrated multiple disease phenotype networks, a gene functional relationship network, and known PD genes to predict novel candidate genes. Then we investigated the translational potential of the predicted genes in drug discovery. In a cross validation analysis, the average rank for 15 known PD genes is within top 0.8 %. We also tested the algorithm with an independent validation set of 669 PD-associated genes detected by genome-wide association studies. The top ranked genes predicted by our approach are enriched for these validation genes. In addition, our approach prioritized the target genes for FDA-approved PD drugs and the drugs that have been tested for PD in clinical trials. Pathway analysis shows that the prioritized drug target genes are closely associated with PD pathogenesis. The result provides empirical evidence that our computational gene prediction approach identifies novel candidate genes for PD, and has the potential to lead to rapid drug discovery.

Twitter Demographics

The data shown below were collected from the profiles of 4 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 19 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 19 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 5 26%
Student > Bachelor 3 16%
Student > Master 3 16%
Other 2 11%
Librarian 1 5%
Other 3 16%
Unknown 2 11%
Readers by discipline Count As %
Engineering 3 16%
Computer Science 3 16%
Neuroscience 2 11%
Agricultural and Biological Sciences 2 11%
Biochemistry, Genetics and Molecular Biology 2 11%
Other 4 21%
Unknown 3 16%

Attention Score in Context

This research output has an Altmetric Attention Score of 12. 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 30 March 2017.
All research outputs
#955,046
of 10,408,647 outputs
Outputs from BMC Genomics
#514
of 6,711 outputs
Outputs of similar age
#36,428
of 257,810 outputs
Outputs of similar age from BMC Genomics
#20
of 286 outputs
Altmetric has tracked 10,408,647 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 90th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 6,711 research outputs from this source. They receive a mean Attention Score of 4.2. This one has done particularly well, scoring higher than 92% 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 257,810 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 286 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 93% of its contemporaries.