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DeepCpG: accurate prediction of single-cell DNA methylation states using deep learning

Overview of attention for article published in Genome Biology (Online Edition), April 2017
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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 (98th percentile)

Citations

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279 Dimensions

Readers on

mendeley
587 Mendeley
citeulike
4 CiteULike
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Title
DeepCpG: accurate prediction of single-cell DNA methylation states using deep learning
Published in
Genome Biology (Online Edition), April 2017
DOI 10.1186/s13059-017-1189-z
Pubmed ID
Authors

Christof Angermueller, Heather J. Lee, Wolf Reik, Oliver Stegle

Abstract

Recent technological advances have enabled DNA methylation to be assayed at single-cell resolution. However, current protocols are limited by incomplete CpG coverage and hence methods to predict missing methylation states are critical to enable genome-wide analyses. We report DeepCpG, a computational approach based on deep neural networks to predict methylation states in single cells. We evaluate DeepCpG on single-cell methylation data from five cell types generated using alternative sequencing protocols. DeepCpG yields substantially more accurate predictions than previous methods. Additionally, we show that the model parameters can be interpreted, thereby providing insights into how sequence composition affects methylation variability.

Twitter Demographics

The data shown below were collected from the profiles of 110 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 587 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 <1%
Belgium 1 <1%
United Kingdom 1 <1%
Japan 1 <1%
Sweden 1 <1%
Unknown 581 99%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 159 27%
Researcher 103 18%
Student > Master 82 14%
Student > Bachelor 37 6%
Student > Doctoral Student 24 4%
Other 78 13%
Unknown 104 18%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 130 22%
Computer Science 113 19%
Agricultural and Biological Sciences 103 18%
Engineering 31 5%
Medicine and Dentistry 15 3%
Other 70 12%
Unknown 125 21%

Attention Score in Context

This research output has an Altmetric Attention Score of 153. 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 17 June 2021.
All research outputs
#175,495
of 19,734,181 outputs
Outputs from Genome Biology (Online Edition)
#83
of 3,858 outputs
Outputs of similar age
#4,832
of 279,776 outputs
Outputs of similar age from Genome Biology (Online Edition)
#1
of 1 outputs
Altmetric has tracked 19,734,181 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 99th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 3,858 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 26.9. This one has done particularly well, scoring higher than 97% 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 279,776 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 98% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them