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Distinguishing between driver and passenger mutations in individual cancer genomes by network enrichment analysis

Overview of attention for article published in BMC Bioinformatics, September 2014
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (79th percentile)
  • Good Attention Score compared to outputs of the same age and source (71st percentile)

Mentioned by

twitter
13 X users
facebook
1 Facebook page

Citations

dimensions_citation
63 Dimensions

Readers on

mendeley
178 Mendeley
citeulike
4 CiteULike
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Title
Distinguishing between driver and passenger mutations in individual cancer genomes by network enrichment analysis
Published in
BMC Bioinformatics, September 2014
DOI 10.1186/1471-2105-15-308
Pubmed ID
Authors

Simon Kebede Merid, Daria Goranskaya, Andrey Alexeyenko

Abstract

In somatic cancer genomes, delineating genuine driver mutations against a background of multiple passenger events is a challenging task. The difficulty of determining function from sequence data and the low frequency of mutations are increasingly hindering the search for novel, less common cancer drivers. The accumulation of extensive amounts of data on somatic point and copy number alterations necessitates the development of systematic methods for driver mutation analysis.

X Demographics

X Demographics

The data shown below were collected from the profiles of 13 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 178 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 1%
United Kingdom 1 <1%
Sweden 1 <1%
Portugal 1 <1%
Unknown 173 97%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 36 20%
Researcher 31 17%
Student > Bachelor 22 12%
Student > Master 22 12%
Student > Postgraduate 14 8%
Other 29 16%
Unknown 24 13%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 53 30%
Agricultural and Biological Sciences 49 28%
Medicine and Dentistry 22 12%
Computer Science 11 6%
Pharmacology, Toxicology and Pharmaceutical Science 3 2%
Other 11 6%
Unknown 29 16%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 7. 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 28 September 2014.
All research outputs
#4,594,331
of 22,764,165 outputs
Outputs from BMC Bioinformatics
#1,762
of 7,273 outputs
Outputs of similar age
#50,622
of 250,225 outputs
Outputs of similar age from BMC Bioinformatics
#30
of 111 outputs
Altmetric has tracked 22,764,165 research outputs across all sources so far. Compared to these this one has done well and is in the 79th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,273 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one has done well, scoring higher than 75% 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 250,225 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 79% of its contemporaries.
We're also able to compare this research output to 111 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 71% of its contemporaries.