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DanQ specifications

Information


Unique identifier OMICS_14322
Name DanQ
Software type Package/Module
Interface Command line interface
Restrictions to use None
Operating system Unix/Linux
Computer skills Advanced
Stability Stable
Requirements
Python, Anaconda, Theano, keras, seya
Maintained Yes

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Documentation


Maintainer


  • person_outline Xiaohui Xie

Publication for DanQ

DanQ citation

library_books

RNA protein binding motifs mining with a new hybrid deep learning based cross domain knowledge integration approach

2017
BMC Bioinformatics
PMCID: 5331642
PMID: 28245811
DOI: 10.1186/s12859-017-1561-8

[…] should be designed specifically for different input data. besides, more advanced network architecture could be designed according to the special characteristics of different input data. for example, danq designed a hybrid convolutional and recurrent neural network to predict the functions from non-coding dna sequences []. it uses cnn to detect regulatory motifs from sequences, followed […]


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DanQ institution(s)
Department of Computer Science University of California, Irvine, CA, USA; Center for Complex Biological Systems University of California, Irvine, CA, USA
DanQ funding source(s)
This work was supported by National Institute of Biomedical Imaging and Bioengineering; University of California, National Research Service Award [EB009418]; Irvine, Center for Complex Biological Systems; National Science Foundation Graduate Research Fellowship [DGE-1321846]; National Institute of Health [HG006870].

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