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IEEE.DeeperBind.Enhancing.Prediction.Of.Sequence.Specificities.
.Of.DNA.Binding.Proteins.2016.RETAiL.eBook-iDiB

PACKS________: idib-deep16
RELEASE DATE_: 2019.02.14
YEAR_________: 2016
LANGUAGE_____: English
RELEASE SIZE_: 0 Mb - 01 files
PAGES________: 6

SOURCE_______: https://ieeexplore.ieee.org/abstract/document/
7822515
PROTECTION___: DRM(removed)
FORMAT_______: .pdf

ISBN CODE____: 10.1109/BIBM.2016.7822515(DOI)
GENRE________: Scientific papers

NOTES________: Enjoy

PLOT_________: Abstract: Transcription factors (TFs) are
macromolecules that bind to cis-regulatory
specific sub-regions of DNA promoters and
initiate transcription. Finding the exact
location of these binding sites (aka motifs)
is important in a variety of domains such as
drug design and development. To address this
need, several in vivo and in vitro techniques
have been developed so far that try to
characterize and predict the binding
specificity of a protein to different DNA
loci. The major problem with these techniques
is that they are not accurate enough in
prediction of the binding affinity and
characterization of the corresponding motifs.
As a result, downstream analysis is required
to uncover the locations where proteins of
interest bind. Here, we propose DeeperBind, a
long short term recurrent convolutional
network for prediction of protein binding
specificities with respect to DNA probes.
DeeperBind can model the positional dynamics
of probe sequences and hence reckons with the
contributions made by individual sub-regions
in DNA sequences, in an effective way.
Moreover, it can be trained and tested on
datasets containing varying-length sequences.
We apply our pipeline to the datasets derived
from protein binding microarrays (PBMs), an
in-vitro high-throughput technology for
quantification of protein-DNA binding
preferences, and present promising results.
To the best of our knowledge, this is the
most accurate pipeline that can predict
binding specificities of DNA sequences from
the data produced by high-throughput
technologies through utilization of the power
of deep learning for feature generation and
positional dynamics modeling.

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