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Computational approaches to identify functional genetic variants in cancer genomes

Overview of attention for article published in Nature Methods, July 2013
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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 (67th percentile)

Mentioned by

blogs
1 blog
twitter
34 X users
wikipedia
1 Wikipedia page
f1000
1 research highlight platform

Citations

dimensions_citation
154 Dimensions

Readers on

mendeley
629 Mendeley
citeulike
24 CiteULike
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Title
Computational approaches to identify functional genetic variants in cancer genomes
Published in
Nature Methods, July 2013
DOI 10.1038/nmeth.2562
Pubmed ID
Abstract

The International Cancer Genome Consortium (ICGC) aims to catalog genomic abnormalities in tumors from 50 different cancer types. Genome sequencing reveals hundreds to thousands of somatic mutations in each tumor but only a minority of these drive tumor progression. We present the result of discussions within the ICGC on how to address the challenge of identifying mutations that contribute to oncogenesis, tumor maintenance or response to therapy, and recommend computational techniques to annotate somatic variants and predict their impact on cancer phenotype.

X Demographics

X Demographics

The data shown below were collected from the profiles of 34 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 629 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 18 3%
Germany 6 <1%
Spain 6 <1%
United Kingdom 4 <1%
Canada 3 <1%
Italy 2 <1%
India 2 <1%
Denmark 2 <1%
Sweden 2 <1%
Other 8 1%
Unknown 576 92%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 131 21%
Researcher 130 21%
Student > Bachelor 127 20%
Student > Master 51 8%
Other 28 4%
Other 81 13%
Unknown 81 13%
Readers by discipline Count As %
Agricultural and Biological Sciences 224 36%
Biochemistry, Genetics and Molecular Biology 169 27%
Computer Science 55 9%
Medicine and Dentistry 40 6%
Engineering 12 2%
Other 36 6%
Unknown 93 15%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 31. 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 06 March 2018.
All research outputs
#1,077,762
of 22,715,151 outputs
Outputs from Nature Methods
#1,382
of 4,896 outputs
Outputs of similar age
#9,913
of 198,188 outputs
Outputs of similar age from Nature Methods
#27
of 82 outputs
Altmetric has tracked 22,715,151 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 4,896 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 34.5. This one has gotten more attention than average, scoring higher than 71% 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 198,188 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 82 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 67% of its contemporaries.