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CoMEt: a statistical approach to identify combinations of mutually exclusive alterations in cancer

Overview of attention for article published in Genome Biology (Online Edition), August 2015
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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 (95th percentile)

Mentioned by

blogs
1 blog
twitter
56 tweeters

Citations

dimensions_citation
150 Dimensions

Readers on

mendeley
147 Mendeley
citeulike
1 CiteULike
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Title
CoMEt: a statistical approach to identify combinations of mutually exclusive alterations in cancer
Published in
Genome Biology (Online Edition), August 2015
DOI 10.1186/s13059-015-0700-7
Pubmed ID
Authors

Mark DM Leiserson, Hsin-Ta Wu, Fabio Vandin, Benjamin J. Raphael

Abstract

Cancer is a heterogeneous disease with different combinations of genetic alterations driving its development in different individuals. We introduce CoMEt, an algorithm to identify combinations of alterations that exhibit a pattern of mutual exclusivity across individuals, often observed for alterations in the same pathway. CoMEt includes an exact statistical test for mutual exclusivity and techniques to perform simultaneous analysis of multiple sets of mutually exclusive and subtype-specific alterations. We demonstrate that CoMEt outperforms existing approaches on simulated and real data. We apply CoMEt to five different cancer types, identifying both known cancer genes and pathways, and novel putative cancer genes.

Twitter Demographics

The data shown below were collected from the profiles of 56 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 147 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 1%
Italy 2 1%
United Kingdom 2 1%
Belgium 1 <1%
Spain 1 <1%
Korea, Republic of 1 <1%
Unknown 138 94%

Demographic breakdown

Readers by professional status Count As %
Researcher 38 26%
Student > Ph. D. Student 33 22%
Student > Master 18 12%
Student > Doctoral Student 9 6%
Student > Bachelor 6 4%
Other 20 14%
Unknown 23 16%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 42 29%
Agricultural and Biological Sciences 34 23%
Computer Science 23 16%
Medicine and Dentistry 12 8%
Mathematics 3 2%
Other 9 6%
Unknown 24 16%

Attention Score in Context

This research output has an Altmetric Attention Score of 37. 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 October 2019.
All research outputs
#760,865
of 19,191,377 outputs
Outputs from Genome Biology (Online Edition)
#653
of 3,808 outputs
Outputs of similar age
#11,841
of 244,782 outputs
Outputs of similar age from Genome Biology (Online Edition)
#1
of 2 outputs
Altmetric has tracked 19,191,377 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 96th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 3,808 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 26.9. This one has done well, scoring higher than 82% 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 244,782 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 95% of its contemporaries.
We're also able to compare this research output to 2 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them