12,682 research outputs found
A Stochastic Hybrid Framework for Driver Behavior Modeling Based on Hierarchical Dirichlet Process
Scalability is one of the major issues for real-world Vehicle-to-Vehicle
network realization. To tackle this challenge, a stochastic hybrid modeling
framework based on a non-parametric Bayesian inference method, i.e.,
hierarchical Dirichlet process (HDP), is investigated in this paper. This
framework is able to jointly model driver/vehicle behavior through forecasting
the vehicle dynamical time-series. This modeling framework could be merged with
the notion of model-based information networking, which is recently proposed in
the vehicular literature, to overcome the scalability challenges in dense
vehicular networks via broadcasting the behavioral models instead of raw
information dissemination. This modeling approach has been applied on several
scenarios from the realistic Safety Pilot Model Deployment (SPMD) driving data
set and the results show a higher performance of this model in comparison with
the zero-hold method as the baseline.Comment: This is the accepted version of the paper in 2018 IEEE 88th Vehicular
Technology Conference (VTC2018-Fall) (references added, title and abstract
modified
A New System for Human MicroRNA functional Evaluation and Network
MicroRNAs are functionally important endogenous non-coding RNAs that silence host genes in animal and plant via destabilizing the mRNAs or preventing the translation. Given the far-reaching implication of microRNA regulation in human health, novel bioinformatics tools are desired to facilitate the mechanistic understanding of microRNA mediated gene regulation, their roles in biological processes, and the functional relevance among microRNAs. However, most state-of-the-art computational methods still focus on the functional study of microRNA targets and there is no e ective strategy to infer the functional similarity among microRNAs. In this study, we developed a new method to quantitatively measure the functional similarity among microRNAs based on the integrated functional annotation data from Gene Ontology, human pathways, and PFam databases. Through analyzing human microRNAs, we further demonstrated the use of the derived microRNA pairwise similarities to discover the cooperative microRNA modules and to construct the genome-scale microRNAmediated gene network in human. The complete results and the similarity assessment system can be freely accessed at (http://sbbi.unl.edu/microRNASim).
Adviser: Juan Cu
Gene expression for simulation of biological tissue
BioDynaMo is a biological processes simulator developed by an international
community of researchers and software engineers working closely with
neuroscientists. The authors have been working on gene expression, i.e. the
process by which the heritable information in a gene - the sequence of DNA base
pairs - is made into a functional gene product, such as protein or RNA.
Typically, gene regulatory models employ either statistical or analytical
approaches, being the former already well understood and broadly used. In this
paper, we utilize analytical approaches representing the regulatory networks by
means of differential equations, such as Euler and Runge-Kutta methods. The two
solutions are implemented and have been submitted for inclusion in the
BioDynaMo project and are compared for accuracy and performance
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Mapping genetic interactions in cancer: a road to rational combination therapies.
The discovery of synthetic lethal interactions between poly (ADP-ribose) polymerase (PARP) inhibitors and BRCA genes, which are involved in homologous recombination, led to the approval of PARP inhibition as a monotherapy for patients with BRCA1/2-mutated breast or ovarian cancer. Studies following the initial observation of synthetic lethality demonstrated that the reach of PARP inhibitors is well beyond just BRCA1/2 mutants. Insights into the mechanisms of action of anticancer drugs are fundamental for the development of targeted monotherapies or rational combination treatments that will synergize to promote cancer cell death and overcome mechanisms of resistance. The development of targeted therapeutic agents is premised on mapping the physical and functional dependencies of mutated genes in cancer. An important part of this effort is the systematic screening of genetic interactions in a variety of cancer types. Until recently, genetic-interaction screens have relied either on the pairwise perturbations of two genes or on the perturbation of genes of interest combined with inhibition by commonly used anticancer drugs. Here, we summarize recent advances in mapping genetic interactions using targeted, genome-wide, and high-throughput genetic screens, and we discuss the therapeutic insights obtained through such screens. We further focus on factors that should be considered in order to develop a robust analysis pipeline. Finally, we discuss the integration of functional interaction data with orthogonal methods and suggest that such approaches will increase the reach of genetic-interaction screens for the development of rational combination therapies
Computational strategies for a system-level understanding of metabolism
Cell metabolism is the biochemical machinery that provides energy and building blocks to sustain life. Understanding its fine regulation is of pivotal relevance in several fields, from metabolic engineering applications to the treatment of metabolic disorders and cancer. Sophisticated computational approaches are needed to unravel the complexity of metabolism. To this aim, a plethora of methods have been developed, yet it is generally hard to identify which computational strategy is most suited for the investigation of a specific aspect of metabolism. This review provides an up-to-date description of the computational methods available for the analysis of metabolic pathways, discussing their main advantages and drawbacks. In particular, attention is devoted to the identification of the appropriate scale and level of accuracy in the reconstruction of metabolic networks, and to the inference of model structure and parameters, especially when dealing with a shortage of experimental measurements. The choice of the proper computational methods to derive in silico data is then addressed, including topological analyses, constraint-based modeling and simulation of the system dynamics. A description of some computational approaches to gain new biological knowledge or to formulate hypotheses is finally provided
An investigation into hazard-centric analysis of complex autonomous systems
This thesis proposes a hypothesis that a conventional, and essentially manual, HAZOP process can be
improved with information obtained with model-based dynamic simulation, using a Monte Carlo
approach, to update a Bayesian Belief model representing the expected relations between cause and
effects – and thereby produce an enhanced HAZOP. The work considers how the expertise of a
hazard and operability study team might be augmented with access to behavioural models,
simulations and belief inference models. This incorporates models of dynamically complex system
behaviour, considering where these might contribute to the expertise of a hazard and operability study
team, and how these might bolster trust in the portrayal of system behaviour. With a questionnaire
containing behavioural outputs from a representative systems model, responses were collected from a
group with relevant domain expertise. From this it is argued that the quality of analysis is dependent
upon the experience and expertise of the participants but this might be artificially augmented using
probabilistic data derived from a system dynamics model. Consequently, Monte Carlo simulations of
an improved exemplar system dynamics model are used to condition a behavioural inference model
and also to generate measures of emergence associated with the deviation parameter used in the study.
A Bayesian approach towards probability is adopted where particular events and combinations of
circumstances are effectively unique or hypothetical, and perhaps irreproducible in practice.
Therefore, it is shown that a Bayesian model, representing beliefs expressed in a hazard and
operability study, conditioned by the likely occurrence of flaw events causing specific deviant
behaviour from evidence observed in the system dynamical behaviour, may combine intuitive
estimates based upon experience and expertise, with quantitative statistical information representing
plausible evidence of safety constraint violation. A further behavioural measure identifies potential
emergent behaviour by way of a Lyapunov Exponent. Together these improvements enhance the
awareness of potential hazard cases
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