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iDeep specifications


Unique identifier OMICS_13505
Name iDeep
Software type Framework/Library
Interface Command line interface
Restrictions to use None
Operating system Unix/Linux
Programming languages Python
Computer skills Advanced
Stability Stable
keras, sklearn
Maintained Yes


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  • person_outline Hong-Bin Shen

Publication for iDeep

iDeep citations


Modeling positional effects of regulatory sequences with spline transformations increases prediction accuracy of deep neural networks

PMCID: 5905632
PMID: 29155928
DOI: 10.1093/bioinformatics/btx727

[…] rbp individually from 20 parameter trials, yielding the best area under the precision–recall curve (aupr) on the validation set., to compare our approach with the rbp binding site prediction model ideep (), we used the same clip dataset, pre-processing code and model code as , both provided by the authors at the clip dataset contains 31 clip experiments […]


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

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

[…] on the sequences, but how to assemble them into the meaningful binding motifs is a topic worth of further investigation., in viewing of these challenges, we propose a deep learning-based framework (ideep) by using a novel hybrid convolutional neural network and deep belief network to predict the rbp interaction sites and motifs on rnas. this new protocol is featured by transforming the original […]


Improved Techniques for Endoscopic Mucosal Resection (EMR) in Colorectal Adenoma

PMCID: 4513797
PMID: 26286120
DOI: 10.1159/000358243

[…] a synthetic product and may have antigenic potential []., there are other proposals for synthetic substances for injection: tran et al. [] presented an injectable drug-eluting elastomeric polymer (ideep), and chandrasekhara et al. [] described a submucosal lifting gel consisting of a combination of biocompatible components. detailed experience with these materials is missing., in contrast […]

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iDeep institution(s)
Department of Veterinary Clinical and Animal Sciences, University of Copenhagen, Copenhagen, Denmark; Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai, China
iDeep funding source(s)
This work was supported by the Science and Technology Commission of Shanghai Municipality (No. 16JC1404300), Fellowship from Faculty of Health and Medical Sciences, University of Copenhagen.

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