DeepEnhancer specifications


Unique identifier OMICS_29958
Name DeepEnhancer
Software type Framework/Library
Interface Command line interface
Restrictions to use None
Operating system Unix/Linux
Computer skills Advanced
Stability Stable
Maintained Yes


No version available


  • person_outline Rui Jiang

Publication for DeepEnhancer

DeepEnhancer citations


Predicting enhancers with deep convolutional neural networks

BMC Bioinformatics
PMCID: 5773911
PMID: 29219068
DOI: 10.1186/s12859-017-1878-3

[…] omics studies, stimulating us to ask the question of whether enhancers can be identified merely by sequence information.Motivated by the above understanding, in this paper, we propose a method called DeepEnhancer to predict enhancers using a deep convolutional neural network (CNN) framework. Specifically, we regard a DNA sequence as a special 1-D image with four channels corresponding to four type […]


Chromatin accessibility prediction via convolutional long short term memory networks with k mer embedding

PMCID: 5870572
PMID: 28881969
DOI: 10.1093/bioinformatics/btx234

[…] g, and showed the benefits of adding more convolutional kernels in learning higher-order sequence features. Moreover, there also exist many other deep learning-based approaches, such as Basset () and DeepEnhancer (), suggesting us that CNNs have strong power in sequence representation and classification.Although having been successfully used, both the above two classes of approaches have their own […]

DeepEnhancer institution(s)
MOE Key Laboratory of Bioinformatics and Bioinformatics Division, TNLIST, Beijing, China; Department of Computer Science and Technology, State Key Lab of Intelligent Technology and Systems, Tsinghua University, Beijing, China; Department of Automation, Tsinghua University, Beijing, China; Program in Computational Biology and Bioinformatics, University of Southern California, Los Angeles, CA, USA
DeepEnhancer funding source(s)
Supported by the National Natural Science Foundation of China (Nos. 61721003, 61573207, 61175002), and the funds from State Key Laboratory of Cardiovascular Disease of China (No. 2016-kf04).

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