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Target inference from collections of genomic intervals

Overview of attention for article published in Proceedings of the National Academy of Sciences of the United States of America, June 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 (95th percentile)
  • Good Attention Score compared to outputs of the same age and source (73rd percentile)

Mentioned by

news
4 news outlets
twitter
1 X user

Citations

dimensions_citation
11 Dimensions

Readers on

mendeley
58 Mendeley
citeulike
3 CiteULike
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Title
Target inference from collections of genomic intervals
Published in
Proceedings of the National Academy of Sciences of the United States of America, June 2013
DOI 10.1073/pnas.1306909110
Pubmed ID
Authors

Alexander Krasnitz, Guoli Sun, Peter Andrews, Michael Wigler

Abstract

Finding regions of the genome that are significantly recurrent in noisy data are a common but difficult problem in present day computational biology. Cores of recurrent events (CORE) is a computational approach to solving this problem that is based on a formalized notion by which "core" intervals explain the observed data, where the number of cores is the "depth" of the explanation. Given that formalization, we implement CORE as a combinatorial optimization procedure with depth chosen from considerations of statistical significance. An important feature of CORE is its ability to explain data with cores of widely varying lengths. We examine the performance of this system with synthetic data, and then provide two demonstrations of its utility with actual data. Applying CORE to a collection of DNA copy number profiles from single cells of a given tumor, we determine tumor population phylogeny and find the features that separate subpopulations. Applying CORE to comparative genomic hybridization data from a large set of tumor samples, we define regions of recurrent copy number aberration in breast cancer.

X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user 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 58 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 3 5%
Unknown 55 95%

Demographic breakdown

Readers by professional status Count As %
Researcher 20 34%
Student > Ph. D. Student 16 28%
Student > Bachelor 5 9%
Professor > Associate Professor 5 9%
Professor 3 5%
Other 8 14%
Unknown 1 2%
Readers by discipline Count As %
Agricultural and Biological Sciences 26 45%
Biochemistry, Genetics and Molecular Biology 13 22%
Medicine and Dentistry 8 14%
Computer Science 5 9%
Mathematics 3 5%
Other 2 3%
Unknown 1 2%
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 24 June 2013.
All research outputs
#1,199,179
of 24,625,114 outputs
Outputs from Proceedings of the National Academy of Sciences of the United States of America
#17,575
of 101,438 outputs
Outputs of similar age
#9,662
of 201,958 outputs
Outputs of similar age from Proceedings of the National Academy of Sciences of the United States of America
#258
of 980 outputs
Altmetric has tracked 24,625,114 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 101,438 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 38.8. 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 201,958 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 980 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 73% of its contemporaries.