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An integrative approach to ortholog prediction for disease-focused and other functional studies

Overview of attention for article published in BMC Bioinformatics, January 2011
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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)
  • High Attention Score compared to outputs of the same age and source (86th percentile)

Mentioned by

blogs
1 blog
twitter
6 tweeters

Citations

dimensions_citation
362 Dimensions

Readers on

mendeley
303 Mendeley
citeulike
3 CiteULike
connotea
1 Connotea
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Title
An integrative approach to ortholog prediction for disease-focused and other functional studies
Published in
BMC Bioinformatics, January 2011
DOI 10.1186/1471-2105-12-357
Pubmed ID
Authors

Yanhui Hu, Ian Flockhart, Arunachalam Vinayagam, Clemens Bergwitz, Bonnie Berger, Norbert Perrimon, Stephanie E Mohr

Abstract

Mapping of orthologous genes among species serves an important role in functional genomics by allowing researchers to develop hypotheses about gene function in one species based on what is known about the functions of orthologs in other species. Several tools for predicting orthologous gene relationships are available. However, these tools can give different results and identification of predicted orthologs is not always straightforward.

Twitter Demographics

The data shown below were collected from the profiles of 6 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 303 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 4 1%
Germany 2 <1%
United Kingdom 1 <1%
France 1 <1%
Denmark 1 <1%
Romania 1 <1%
Japan 1 <1%
Canada 1 <1%
Unknown 291 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 76 25%
Researcher 64 21%
Student > Master 40 13%
Student > Bachelor 25 8%
Professor 16 5%
Other 48 16%
Unknown 34 11%
Readers by discipline Count As %
Agricultural and Biological Sciences 102 34%
Biochemistry, Genetics and Molecular Biology 89 29%
Neuroscience 25 8%
Medicine and Dentistry 13 4%
Computer Science 13 4%
Other 23 8%
Unknown 38 13%

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 04 April 2016.
All research outputs
#1,251,431
of 12,373,386 outputs
Outputs from BMC Bioinformatics
#512
of 4,576 outputs
Outputs of similar age
#9,923
of 91,158 outputs
Outputs of similar age from BMC Bioinformatics
#5
of 22 outputs
Altmetric has tracked 12,373,386 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 4,576 research outputs from this source. They receive a mean Attention Score of 4.9. 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 91,158 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 22 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 86% of its contemporaries.