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Genome-wide prediction of cis-regulatory regions using supervised deep learning methods

Overview of attention for article published in BMC Bioinformatics, May 2018
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (80th percentile)
  • High Attention Score compared to outputs of the same age and source (95th percentile)

Mentioned by

twitter
8 tweeters
patent
2 patents
wikipedia
2 Wikipedia pages

Citations

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72 Dimensions

Readers on

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286 Mendeley
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Title
Genome-wide prediction of cis-regulatory regions using supervised deep learning methods
Published in
BMC Bioinformatics, May 2018
DOI 10.1186/s12859-018-2187-1
Pubmed ID
Authors

Yifeng Li, Wenqiang Shi, Wyeth W. Wasserman

Abstract

In the human genome, 98% of DNA sequences are non-protein-coding regions that were previously disregarded as junk DNA. In fact, non-coding regions host a variety of cis-regulatory regions which precisely control the expression of genes. Thus, Identifying active cis-regulatory regions in the human genome is critical for understanding gene regulation and assessing the impact of genetic variation on phenotype. The developments of high-throughput sequencing and machine learning technologies make it possible to predict cis-regulatory regions genome wide. Based on rich data resources such as the Encyclopedia of DNA Elements (ENCODE) and the Functional Annotation of the Mammalian Genome (FANTOM) projects, we introduce DECRES based on supervised deep learning approaches for the identification of enhancer and promoter regions in the human genome. Due to their ability to discover patterns in large and complex data, the introduction of deep learning methods enables a significant advance in our knowledge of the genomic locations of cis-regulatory regions. Using models for well-characterized cell lines, we identify key experimental features that contribute to the predictive performance. Applying DECRES, we delineate locations of 300,000 candidate enhancers genome wide (6.8% of the genome, of which 40,000 are supported by bidirectional transcription data), and 26,000 candidate promoters (0.6% of the genome). The predicted annotations of cis-regulatory regions will provide broad utility for genome interpretation from functional genomics to clinical applications. The DECRES model demonstrates potentials of deep learning technologies when combined with high-throughput sequencing data, and inspires the development of other advanced neural network models for further improvement of genome annotations.

Twitter Demographics

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Mendeley readers

The data shown below were compiled from readership statistics for 286 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 4 1%
Japan 1 <1%
France 1 <1%
Canada 1 <1%
Unknown 279 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 73 26%
Researcher 42 15%
Student > Master 40 14%
Student > Bachelor 26 9%
Student > Postgraduate 12 4%
Other 45 16%
Unknown 48 17%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 69 24%
Agricultural and Biological Sciences 58 20%
Computer Science 48 17%
Engineering 16 6%
Medicine and Dentistry 7 2%
Other 29 10%
Unknown 59 21%

Attention Score in Context

This research output has an Altmetric Attention Score of 10. 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 21 October 2021.
All research outputs
#2,724,001
of 21,470,856 outputs
Outputs from BMC Bioinformatics
#1,001
of 6,967 outputs
Outputs of similar age
#57,494
of 300,046 outputs
Outputs of similar age from BMC Bioinformatics
#2
of 22 outputs
Altmetric has tracked 21,470,856 research outputs across all sources so far. Compared to these this one has done well and is in the 86th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 6,967 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one has done well, scoring higher than 85% 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 300,046 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 80% of its contemporaries.
We're also able to compare this research output to 22 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 95% of its contemporaries.