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Phen-Gen: combining phenotype and genotype to analyze rare disorders

Overview of attention for article published in Nature Methods, August 2014
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (94th percentile)
  • Good Attention Score compared to outputs of the same age and source (68th percentile)

Mentioned by

news
1 news outlet
blogs
1 blog
twitter
25 tweeters
facebook
2 Facebook pages
googleplus
1 Google+ user

Citations

dimensions_citation
100 Dimensions

Readers on

mendeley
223 Mendeley
citeulike
4 CiteULike
Title
Phen-Gen: combining phenotype and genotype to analyze rare disorders
Published in
Nature Methods, August 2014
DOI 10.1038/nmeth.3046
Pubmed ID
Authors

Asif Javed, Saloni Agrawal, Pauline C Ng

Abstract

We introduce Phen-Gen, a method that combines patients' disease symptoms and sequencing data with prior domain knowledge to identify the causative genes for rare disorders. Simulations revealed that the causal variant was ranked first in 88% of cases when it was a coding variant-a 52% advantage over a genotype-only approach-and Phen-Gen outperformed other existing prediction methods by 13-58%. If disease etiology was unknown, the causal variant was assigned the top rank in 71% of simulations. Phen-Gen is available at http://phen-gen.org/.

Twitter Demographics

The data shown below were collected from the profiles of 25 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 223 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 6 3%
Brazil 4 2%
United States 3 1%
Italy 3 1%
Spain 2 <1%
Hong Kong 1 <1%
Norway 1 <1%
Korea, Republic of 1 <1%
France 1 <1%
Other 6 3%
Unknown 195 87%

Demographic breakdown

Readers by professional status Count As %
Researcher 66 30%
Student > Ph. D. Student 55 25%
Student > Master 26 12%
Other 15 7%
Professor > Associate Professor 13 6%
Other 30 13%
Unknown 18 8%
Readers by discipline Count As %
Agricultural and Biological Sciences 85 38%
Biochemistry, Genetics and Molecular Biology 48 22%
Computer Science 28 13%
Medicine and Dentistry 20 9%
Neuroscience 6 3%
Other 12 5%
Unknown 24 11%

Attention Score in Context

This research output has an Altmetric Attention Score of 30. 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 15 February 2018.
All research outputs
#628,556
of 14,573,111 outputs
Outputs from Nature Methods
#909
of 3,989 outputs
Outputs of similar age
#12,253
of 215,903 outputs
Outputs of similar age from Nature Methods
#30
of 95 outputs
Altmetric has tracked 14,573,111 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 95th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 3,989 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 27.6. This one has done well, scoring higher than 77% 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 215,903 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 94% of its contemporaries.
We're also able to compare this research output to 95 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 68% of its contemporaries.