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Predicting enhancers with deep convolutional neural networks

Overview of attention for article published in BMC Bioinformatics, December 2017
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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 (70th percentile)
  • Good Attention Score compared to outputs of the same age and source (73rd percentile)

Mentioned by

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10 tweeters

Citations

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24 Dimensions

Readers on

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114 Mendeley
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Title
Predicting enhancers with deep convolutional neural networks
Published in
BMC Bioinformatics, December 2017
DOI 10.1186/s12859-017-1878-3
Pubmed ID
Authors

Xu Min, Wanwen Zeng, Shengquan Chen, Ning Chen, Ting Chen, Rui Jiang

Abstract

With the rapid development of deep sequencing techniques in the recent years, enhancers have been systematically identified in such projects as FANTOM and ENCODE, forming genome-wide landscapes in a series of human cell lines. Nevertheless, experimental approaches are still costly and time consuming for large scale identification of enhancers across a variety of tissues under different disease status, making computational identification of enhancers indispensable. To facilitate the identification of enhancers, we propose a computational framework, named DeepEnhancer, to distinguish enhancers from background genomic sequences. Our method purely relies on DNA sequences to predict enhancers in an end-to-end manner by using a deep convolutional neural network (CNN). We train our deep learning model on permissive enhancers and then adopt a transfer learning strategy to fine-tune the model on enhancers specific to a cell line. Results demonstrate the effectiveness and efficiency of our method in the classification of enhancers against random sequences, exhibiting advantages of deep learning over traditional sequence-based classifiers. We then construct a variety of neural networks with different architectures and show the usefulness of such techniques as max-pooling and batch normalization in our method. To gain the interpretability of our approach, we further visualize convolutional kernels as sequence logos and successfully identify similar motifs in the JASPAR database. DeepEnhancer enables the identification of novel enhancers using only DNA sequences via a highly accurate deep learning model. The proposed computational framework can also be applied to similar problems, thereby prompting the use of machine learning methods in life sciences.

Twitter Demographics

The data shown below were collected from the profiles of 10 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 114 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 114 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 34 30%
Student > Master 19 17%
Researcher 10 9%
Student > Bachelor 7 6%
Professor > Associate Professor 5 4%
Other 12 11%
Unknown 27 24%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 36 32%
Computer Science 22 19%
Agricultural and Biological Sciences 12 11%
Engineering 5 4%
Mathematics 3 3%
Other 7 6%
Unknown 29 25%

Attention Score in Context

This research output has an Altmetric Attention Score of 5. 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 12 February 2018.
All research outputs
#3,081,480
of 12,498,628 outputs
Outputs from BMC Bioinformatics
#1,352
of 4,655 outputs
Outputs of similar age
#109,146
of 379,427 outputs
Outputs of similar age from BMC Bioinformatics
#116
of 438 outputs
Altmetric has tracked 12,498,628 research outputs across all sources so far. Compared to these this one has done well and is in the 75th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 4,655 research outputs from this source. They receive a mean Attention Score of 4.9. This one has gotten more attention than average, scoring higher than 70% 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 379,427 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 70% of its contemporaries.
We're also able to compare this research output to 438 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 73% of its contemporaries.