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Learning smoothing models of copy number profiles using breakpoint annotations

Overview of attention for article published in BMC Bioinformatics, May 2013
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2 tweeters

Citations

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18 Dimensions

Readers on

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36 Mendeley
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2 CiteULike
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Title
Learning smoothing models of copy number profiles using breakpoint annotations
Published in
BMC Bioinformatics, May 2013
DOI 10.1186/1471-2105-14-164
Pubmed ID
Authors

Toby Dylan Hocking, Gudrun Schleiermacher, Isabelle Janoueix-Lerosey, Valentina Boeva, Julie Cappo, Olivier Delattre, Francis Bach, Jean-Philippe Vert

Abstract

Many models have been proposed to detect copy number alterations in chromosomal copy number profiles, but it is usually not obvious to decide which is most effective for a given data set. Furthermore, most methods have a smoothing parameter that determines the number of breakpoints and must be chosen using various heuristics.

Twitter Demographics

The data shown below were collected from the profiles of 2 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 36 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 6%
Sweden 1 3%
Canada 1 3%
Unknown 32 89%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 14 39%
Researcher 7 19%
Other 4 11%
Student > Bachelor 3 8%
Professor 2 6%
Other 4 11%
Unknown 2 6%
Readers by discipline Count As %
Agricultural and Biological Sciences 13 36%
Mathematics 7 19%
Computer Science 5 14%
Medicine and Dentistry 3 8%
Biochemistry, Genetics and Molecular Biology 3 8%
Other 3 8%
Unknown 2 6%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 22 May 2013.
All research outputs
#9,153,042
of 14,573,111 outputs
Outputs from BMC Bioinformatics
#3,750
of 5,420 outputs
Outputs of similar age
#84,132
of 153,675 outputs
Outputs of similar age from BMC Bioinformatics
#2
of 3 outputs
Altmetric has tracked 14,573,111 research outputs across all sources so far. This one is in the 24th percentile – i.e., 24% of other outputs scored the same or lower than it.
So far Altmetric has tracked 5,420 research outputs from this source. They receive a mean Attention Score of 4.9. This one is in the 22nd percentile – i.e., 22% of its peers scored the same or lower than it.
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 153,675 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 33rd percentile – i.e., 33% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 3 others from the same source and published within six weeks on either side of this one.