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3CPET: finding co-factor complexes from ChIA-PET data using a hierarchical Dirichlet process

Overview of attention for article published in Genome Biology, December 2015
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (79th percentile)

Mentioned by

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14 X users

Citations

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21 Dimensions

Readers on

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57 Mendeley
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5 CiteULike
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Title
3CPET: finding co-factor complexes from ChIA-PET data using a hierarchical Dirichlet process
Published in
Genome Biology, December 2015
DOI 10.1186/s13059-015-0851-6
Pubmed ID
Authors

Mohamed Nadhir Djekidel, Zhengyu Liang, Qi Wang, Zhirui Hu, Guipeng Li, Yang Chen, Michael Q. Zhang

Abstract

Various efforts have been made to elucidate the cooperating proteins involved in maintaining chromatin interactions; however, many are still unknown. Here, we present 3CPET, a tool based on a non-parametric Bayesian approach, to infer the set of the most probable protein complexes involved in maintaining chromatin interactions and the regions that they may control, making it a valuable downstream analysis tool in chromatin conformation studies. 3CPET does so by combining data from ChIA-PET, transcription factor binding sites, and protein interactions. 3CPET results show biologically significant and accurate predictions when validated against experimental and simulation data.

X Demographics

X Demographics

The data shown below were collected from the profiles of 14 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 57 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Japan 2 4%
Hungary 1 2%
China 1 2%
Germany 1 2%
Russia 1 2%
United States 1 2%
Unknown 50 88%

Demographic breakdown

Readers by professional status Count As %
Researcher 15 26%
Student > Ph. D. Student 10 18%
Student > Bachelor 6 11%
Student > Master 6 11%
Professor > Associate Professor 4 7%
Other 8 14%
Unknown 8 14%
Readers by discipline Count As %
Agricultural and Biological Sciences 23 40%
Biochemistry, Genetics and Molecular Biology 12 21%
Computer Science 6 11%
Engineering 2 4%
Chemical Engineering 1 2%
Other 2 4%
Unknown 11 19%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 7. 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 25 January 2016.
All research outputs
#5,211,074
of 25,374,647 outputs
Outputs from Genome Biology
#2,846
of 4,467 outputs
Outputs of similar age
#80,710
of 396,428 outputs
Outputs of similar age from Genome Biology
#57
of 72 outputs
Altmetric has tracked 25,374,647 research outputs across all sources so far. Compared to these this one has done well and is in the 79th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 4,467 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 27.6. This one is in the 36th percentile – i.e., 36% 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 396,428 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 79% of its contemporaries.
We're also able to compare this research output to 72 others from the same source and published within six weeks on either side of this one. This one is in the 19th percentile – i.e., 19% of its contemporaries scored the same or lower than it.