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Inferring novel gene-disease associations using Medical Subject Heading Over-representation Profiles

Overview of attention for article published in Genome Medicine, September 2012
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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 (89th percentile)
  • Good Attention Score compared to outputs of the same age and source (70th percentile)

Mentioned by

blogs
1 blog
twitter
7 X users

Citations

dimensions_citation
25 Dimensions

Readers on

mendeley
40 Mendeley
citeulike
2 CiteULike
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Title
Inferring novel gene-disease associations using Medical Subject Heading Over-representation Profiles
Published in
Genome Medicine, September 2012
DOI 10.1186/gm376
Pubmed ID
Authors

Warren A Cheung, BF Francis Ouellette, Wyeth W Wasserman

Abstract

MEDLINE(®)/PubMed(®) currently indexes over 18 million biomedical articles, providing unprecedented opportunities and challenges for text analysis. Using Medical Subject Heading Over-representation Profiles (MeSHOPs), an entity of interest can be robustly summarized, quantitatively identifying associated biomedical terms and predicting novel indirect associations.

X Demographics

X Demographics

The data shown below were collected from the profiles of 7 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 40 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 3%
Spain 1 3%
United States 1 3%
Unknown 37 93%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 9 23%
Researcher 9 23%
Student > Bachelor 5 13%
Other 4 10%
Student > Postgraduate 3 8%
Other 8 20%
Unknown 2 5%
Readers by discipline Count As %
Computer Science 10 25%
Agricultural and Biological Sciences 10 25%
Medicine and Dentistry 6 15%
Biochemistry, Genetics and Molecular Biology 3 8%
Engineering 2 5%
Other 7 18%
Unknown 2 5%
Attention Score in Context

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 04 November 2012.
All research outputs
#2,787,402
of 25,374,917 outputs
Outputs from Genome Medicine
#637
of 1,585 outputs
Outputs of similar age
#19,616
of 191,378 outputs
Outputs of similar age from Genome Medicine
#5
of 17 outputs
Altmetric has tracked 25,374,917 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 1,585 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 26.8. This one has gotten more attention than average, scoring higher than 59% 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 191,378 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 89% of its contemporaries.
We're also able to compare this research output to 17 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 70% of its contemporaries.