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DeepBind specifications


Unique identifier OMICS_30615
Name DeepBind
Interface Web user interface
Restrictions to use None
Computer skills Basic
Stability Stable
Maintained Yes


  • person_outline Brendan Frey

Publication for DeepBind

DeepBind citations


Prediction of enhancer promoter interactions via natural language processing

BMC Genomics
PMCID: 5954283
PMID: 29764360
DOI: 10.1186/s12864-018-4459-6

[…] ge processing (NLP). As is well known, Convolutional Neural Network (CNN) is a powerful deep learning model in computer vision area. Inspired by deep learning applied in image processing, DeepSEA and DeepBind first regard DNA sequences as binary images through one-hot encoding. They both preprocess the DNA sequences by transforming them into 4xL images (L is the length of a sequence), and then use […]


Mutation hotspots at CTCF binding sites coupled to chromosomal instability in gastrointestinal cancers

Nat Commun
PMCID: 5906695
PMID: 29670109
DOI: 10.1038/s41467-018-03828-2

[…] CF motifs, could disrupt context-specific binding of CTCF.We also examined the possibility that mutations in the flanking regions of CTCF motifs create or disrupt binding motifs of other TFs. We used DeepBind to predict the binding scores of wildtype and mutated sequences for 472 transcription factors. However, we only found mutations at three CBS sites with predicted change in TF binding (Supplem […]


Opportunities and obstacles for deep learning in biology and medicine

PMCID: 5938574
PMID: 29618526
DOI: 10.1098/rsif.2017.0387

[…] niscent of PWMs from a one-hot encoding of the raw input DNA sequences. DeepBind outperformed several state-of-the-art methods from the DREAM5 in vitro TF-DNA motif recognition challenge []. Although DeepBind was also applied to RNA-binding proteins, in general, RNA binding is a separate problem [] and accurate models will need to account for RNA secondary structure. Following DeepBind, several op […]


Accurate and sensitive quantification of protein DNA binding affinity

Proc Natl Acad Sci U S A
PMCID: 5910815
PMID: 29610332
DOI: 10.1073/pnas.1714376115

[…] formance (details about the training of NRLB on HT-SELEX data are provided in and SI Appendix, Fig. S11). Comparison of the area under the curve (AUC) for various TFs shows that NRLB does as well as DeepBind [ and SI Appendix, Figs. S12 (overview) and S14 (details)]. However, the binary ChIP-seq peak classification metric may not accurately distinguish between weak and nonspecific binding. To qua […]


Inferring RNA sequence preferences for poorly studied RNA binding proteins based on co evolution

BMC Bioinformatics
PMCID: 5848454
PMID: 29529991
DOI: 10.1186/s12859-018-2091-8

[…] or poorly characterized RBPs, first we applied our KNN algorithm to the RBPs with known binding data and demonstrated the ability of our algorithm. We compared the performance of our method with 1NN, DeepBind and RCK on the InVitro and InVivoRay datasets. […]


The rise and fall of machine learning methods in biomedical research

PMCID: 5760972
PMID: 29375816
DOI: 10.5256/f1000research.14114.r28048

[…] aracterizing chromatin states by employing unsupervised approaches such as chromHMM ( ), predicting sequence specificities of DNA- and RNA-binding proteins using convolutional neural networks such as DeepBind ( ), and employing a combination of supervised and unsupervised approach to determine the genetic and epigenetic contributors of antibody repertoire diversity ( ). Nowadays it is almost impos […]


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DeepBind institution(s)
Department of Electrical and Computer Engineering, University of Toronto, Toronto, ON, Canada; Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada; Canadian Institute for Advanced Research, Programs on Genetic Networks and Neural Computation, Toronto, ON, Canada; Center for Autoimmune Genomics and Etiology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA; Divisions of Biomedical Informatics and Developmental Biology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA
DeepBind funding source(s)
Supported by a grant from the Canadian Institutes of Health Research (OGP-106690), a John C. Polanyi Fellowship Grant, the Canadian Institutes for Advanced Research, a joint Autism Research Training and NeuroDevNet Fellowship, and a Fellowship from the Natural Science and Engineering Research Council of Canada.

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