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DIVAN: accurate identification of non-coding disease-specific risk variants using multi-omics profiles

Overview of attention for article published in Genome Biology, December 2016
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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 (77th percentile)

Mentioned by

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14 X users

Citations

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69 Dimensions

Readers on

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84 Mendeley
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2 CiteULike
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Title
DIVAN: accurate identification of non-coding disease-specific risk variants using multi-omics profiles
Published in
Genome Biology, December 2016
DOI 10.1186/s13059-016-1112-z
Pubmed ID
Authors

Li Chen, Peng Jin, Zhaohui S. Qin

Abstract

Understanding the link between non-coding sequence variants, identified in genome-wide association studies, and the pathophysiology of complex diseases remains challenging due to a lack of annotations in non-coding regions. To overcome this, we developed DIVAN, a novel feature selection and ensemble learning framework, which identifies disease-specific risk variants by leveraging a comprehensive collection of genome-wide epigenomic profiles across cell types and factors, along with other static genomic features. DIVAN accurately and robustly recognizes non-coding disease-specific risk variants under multiple testing scenarios; among all the features, histone marks, especially those marks associated with repressed chromatin, are often more informative than others.

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

Geographical breakdown

Country Count As %
United Kingdom 1 1%
Italy 1 1%
Unknown 82 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 20 24%
Researcher 19 23%
Professor > Associate Professor 5 6%
Student > Master 5 6%
Student > Doctoral Student 4 5%
Other 14 17%
Unknown 17 20%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 29 35%
Agricultural and Biological Sciences 10 12%
Computer Science 10 12%
Medicine and Dentistry 4 5%
Neuroscience 4 5%
Other 8 10%
Unknown 19 23%
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 03 April 2017.
All research outputs
#5,122,884
of 25,373,627 outputs
Outputs from Genome Biology
#2,824
of 4,467 outputs
Outputs of similar age
#92,524
of 420,276 outputs
Outputs of similar age from Genome Biology
#43
of 59 outputs
Altmetric has tracked 25,373,627 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 420,276 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 77% of its contemporaries.
We're also able to compare this research output to 59 others from the same source and published within six weeks on either side of this one. This one is in the 27th percentile – i.e., 27% of its contemporaries scored the same or lower than it.