↓ Skip to main content

Segmentor3IsBack: an R package for the fast and exact segmentation of Seq-data

Overview of attention for article published in Algorithms for Molecular Biology, March 2014
Altmetric Badge

About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#31 of 264)
  • High Attention Score compared to outputs of the same age (82nd percentile)
  • Good Attention Score compared to outputs of the same age and source (77th percentile)

Mentioned by

blogs
1 blog
twitter
1 X user

Citations

dimensions_citation
28 Dimensions

Readers on

mendeley
43 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Segmentor3IsBack: an R package for the fast and exact segmentation of Seq-data
Published in
Algorithms for Molecular Biology, March 2014
DOI 10.1186/1748-7188-9-6
Pubmed ID
Authors

Alice Cleynen, Michel Koskas, Emilie Lebarbier, Guillem Rigaill, Stéphane Robin

Abstract

Change point problems arise in many genomic analyses such as the detection of copy number variations or the detection of transcribed regions. The expanding Next Generation Sequencing technologies now allow to locate change points at the nucleotide resolution.

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 43 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 2%
France 1 2%
Unknown 41 95%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 11 26%
Researcher 10 23%
Student > Master 5 12%
Professor 4 9%
Student > Bachelor 3 7%
Other 4 9%
Unknown 6 14%
Readers by discipline Count As %
Agricultural and Biological Sciences 14 33%
Biochemistry, Genetics and Molecular Biology 6 14%
Mathematics 6 14%
Engineering 4 9%
Business, Management and Accounting 1 2%
Other 3 7%
Unknown 9 21%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 8. 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 31 July 2014.
All research outputs
#3,971,406
of 22,749,166 outputs
Outputs from Algorithms for Molecular Biology
#31
of 264 outputs
Outputs of similar age
#39,617
of 220,763 outputs
Outputs of similar age from Algorithms for Molecular Biology
#2
of 9 outputs
Altmetric has tracked 22,749,166 research outputs across all sources so far. Compared to these this one has done well and is in the 82nd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 264 research outputs from this source. They receive a mean Attention Score of 3.2. This one has done well, scoring higher than 87% 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 220,763 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 82% of its contemporaries.
We're also able to compare this research output to 9 others from the same source and published within six weeks on either side of this one. This one has scored higher than 7 of them.