30,200 research outputs found
Multi-task Deep Neural Networks in Automated Protein Function Prediction
In recent years, deep learning algorithms have outperformed the state-of-the
art methods in several areas thanks to the efficient methods for training and
for preventing overfitting, advancement in computer hardware, the availability
of vast amount data. The high performance of multi-task deep neural networks in
drug discovery has attracted the attention to deep learning algorithms in
bioinformatics area. Here, we proposed a hierarchical multi-task deep neural
network architecture based on Gene Ontology (GO) terms as a solution to protein
function prediction problem and investigated various aspects of the proposed
architecture by performing several experiments. First, we showed that there is
a positive correlation between performance of the system and the size of
training datasets. Second, we investigated whether the level of GO terms on GO
hierarchy related to their performance. We showed that there is no relation
between the depth of GO terms on GO hierarchy and their performance. In
addition, we included all annotations to the training of a set of GO terms to
investigate whether including noisy data to the training datasets change the
performance of the system. The results showed that including less reliable
annotations in training of deep neural networks increased the performance of
the low performed GO terms, significantly. We evaluated the performance of the
system using hierarchical evaluation method. Mathews correlation coefficient
was calculated as 0.75, 0.49 and 0.63 for molecular function, biological
process and cellular component categories, respectively. We showed that deep
learning algorithms have a great potential in protein function prediction area.
We plan to further improve the DEEPred by including other types of annotations
from various biological data sources. We plan to construct DEEPred as an open
access online tool.Comment: 19 pages, 4 figures, 4 table
Data-driven Flood Emulation: Speeding up Urban Flood Predictions by Deep Convolutional Neural Networks
Computational complexity has been the bottleneck of applying physically-based
simulations on large urban areas with high spatial resolution for efficient and
systematic flooding analyses and risk assessments. To address this issue of
long computational time, this paper proposes that the prediction of maximum
water depth rasters can be considered as an image-to-image translation problem
where the results are generated from input elevation rasters using the
information learned from data rather than by conducting simulations, which can
significantly accelerate the prediction process. The proposed approach was
implemented by a deep convolutional neural network trained on flood simulation
data of 18 designed hyetographs on three selected catchments. Multiple tests
with both designed and real rainfall events were performed and the results show
that the flood predictions by neural network uses only 0.5 % of time comparing
with physically-based approaches, with promising accuracy and ability of
generalizations. The proposed neural network can also potentially be applied to
different but relevant problems including flood predictions for urban layout
planning
Prospects for Theranostics in Neurosurgical Imaging: Empowering Confocal Laser Endomicroscopy Diagnostics via Deep Learning
Confocal laser endomicroscopy (CLE) is an advanced optical fluorescence
imaging technology that has the potential to increase intraoperative precision,
extend resection, and tailor surgery for malignant invasive brain tumors
because of its subcellular dimension resolution. Despite its promising
diagnostic potential, interpreting the gray tone fluorescence images can be
difficult for untrained users. In this review, we provide a detailed
description of bioinformatical analysis methodology of CLE images that begins
to assist the neurosurgeon and pathologist to rapidly connect on-the-fly
intraoperative imaging, pathology, and surgical observation into a
conclusionary system within the concept of theranostics. We present an overview
and discuss deep learning models for automatic detection of the diagnostic CLE
images and discuss various training regimes and ensemble modeling effect on the
power of deep learning predictive models. Two major approaches reviewed in this
paper include the models that can automatically classify CLE images into
diagnostic/nondiagnostic, glioma/nonglioma, tumor/injury/normal categories and
models that can localize histological features on the CLE images using weakly
supervised methods. We also briefly review advances in the deep learning
approaches used for CLE image analysis in other organs. Significant advances in
speed and precision of automated diagnostic frame selection would augment the
diagnostic potential of CLE, improve operative workflow and integration into
brain tumor surgery. Such technology and bioinformatics analytics lend
themselves to improved precision, personalization, and theranostics in brain
tumor treatment.Comment: See the final version published in Frontiers in Oncology here:
https://www.frontiersin.org/articles/10.3389/fonc.2018.00240/ful
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