768 research outputs found
Prediction of Deleterious Nonsynonymous Single-Nucleotide Polymorphism for Human Diseases
The identification of genetic variants that are responsible for human inherited diseases is a fundamental problem in human and medical genetics. As a typical type of genetic variation, nonsynonymous single-nucleotide polymorphisms (nsSNPs) occurring in protein coding regions may alter the encoded amino acid, potentially affect protein structure and function, and further result in human inherited diseases. Therefore, it is of great importance to develop computational approaches to facilitate the discrimination of deleterious nsSNPs from neutral ones. In this paper, we review databases that collect nsSNPs and summarize computational methods for the identification of deleterious nsSNPs. We classify the existing methods for characterizing nsSNPs into three categories (sequence based, structure based, and annotation based), and we introduce machine learning models for the prediction of deleterious nsSNPs. We further discuss methods for identifying deleterious nsSNPs in noncoding variants and those for dealing with rare variants
Risk-Averse Optimization for Resilience Enhancement of Complex Engineering Systems under Uncertainties
With the growth of complexity and extent, large scale interconnected network
systems, e.g. transportation networks or infrastructure networks, become more
vulnerable towards external disruptions. Hence, managing potential disruptive
events during the design, operating, and recovery phase of an engineered system
therefore improving the system's resilience is an important yet challenging
task. In order to ensure system resilience after the occurrence of failure
events, this study proposes a mixed-integer linear programming (MILP) based
restoration framework using heterogeneous dispatchable agents. Scenario-based
stochastic optimization (SO) technique is adopted to deal with the inherent
uncertainties imposed on the recovery process from nature. Moreover, different
from conventional SO using deterministic equivalent formulations, additional
risk measure is implemented for this study because of the temporal sparsity of
the decision making in applications such as the recovery from extreme events.
The resulting restoration framework involves a large-scale MILP problem and
thus an adequate decomposition technique, i.e. modified Lagrangian dual
decomposition, is also employed in order to achieve tractable computational
complexity. Case study results based on the IEEE 37-bus test feeder demonstrate
the benefits of using the proposed framework for resilience improvement as well
as the advantages of adopting SO formulations
Deep-learning assisted reduced order model for high-dimensional flow prediction from sparse data
The reconstruction and prediction of full-state flows from sparse data are of
great scientific and engineering significance yet remain challenging,
especially in applications where data are sparse and/or subjected to noise. To
this end, this study proposes a deep-learning assisted non-intrusive reduced
order model (named DCDMD) for high-dimensional flow prediction from sparse
data. Based on the compressed sensing (CS)-Dynamic Mode Decomposition (DMD),
the DCDMD model is distinguished by two novelties. Firstly, a sparse matrix is
defined to overcome the strict random distribution condition of sensor
locations in CS, thus allowing flexible sensor deployments and requiring very
few sensors. Secondly, a deep-learning-based proxy is invoked to acquire
coherent flow modes from the sparse data of high-dimensional flows, thereby
addressing the issue of defining sparsity and the stringent incoherence
condition in the conventional CSDMD. The two advantageous features, combined
with the fact that the model retains flow physics in the online stage, lead to
significant enhancements in accuracy and efficiency, as well as superior
insensitivity to data noises (i.e., robustness), in both reconstruction and
prediction of full-state flows. These are demonstrated by three benchmark
examples, i.e., cylinder wake, weekly-mean sea surface temperature and
isotropic turbulence in a periodic square area.Comment: 36 Pages, 23 Figures, 5 Table
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