9190159
Dynamic Bayesian Network (DBN) with Structure Expectation Maximization (SEM) for Modeling of Gene Network from Time Series Gene Expression Data
Abstract Exploring gene regulatory network is a key topic in molecular biology. In this paper, we present a new dynamic Bayesian network (DBN) framework embedded with structural expectation maximization (SEM) to model gene relationship. It is well-suited for analyzing the time-series data and can deal with cyclical structures that can not be tackled by static Bayesian network. We applied the new method to learning the regulatory network and the metabolic pathway from Saccharomyces Cerevisiae cell cycle gene expression data. The results show that the proposed method is capable of handling missing values in expression data sets, and the inference accuracy can further be improved. Keyword: Microarrays; Gene regulatory networks; Dynamic Bayesian network; Structural expectation maximization 1. Introduction The establishment of gene regulatory network is critical to the understanding of the genetic regulation process. This problem has become an important challenge in recent years. The invention of microarray technology is viewed as a milestone, which helps scientist measure expression levels of thousands of genes simultaneously. Several methods have been presented so far to learn gene network from microarray data, such as Boolean networks [2, 3], differential equations [4, 5], and Bayesian networks [6-8]. Among all of them, Bayesian network based approach has received a lot of attention because of the probabilistic nature of this model. It can be used to learn causal relationship and particularly, combine it with prior knowledge readily. However, there exist a lot of shortcomings for the static version of Bayesian network. First, it is unable to capture the temporal information. Second, it is impossible to model cyclic network, which is often considered to be an accurate description of real gene regulation mechanism. In this paper, we employ dynamic Bayesian network (DBN) [1, 9,10], instead of its earlier static version, to model a gene network with cyclic regulation. In general, the DBN is well-suited for characterizing time-series gene expression data. Owing to the limitation of experimental condition, there are many missing values in the gene expression data sets, which usually have an impact on the inference accuracy. To address this problem, we propose a new DBN model embedded with
15661895
Robust Distributed Localization with Data Inference for Wireless Sensor Networks
We consider the range-based localization with highly insufficient inter-node distance measurements in Wireless Sensor Networks (WSNs). With an error model established, a novel distributed probabilistic localization approach which exploits the network constraints through statistical inference on missing distances is proposed. The advantage of the new approach over traditional methods is proved analytically and further confirmed by extensive simulations.
5444016
Arbitrary warped document image restoration based on segmentation and Thin-Plate Splines
Warping is a common appearance in camera captured document images. It is the primary factor that makes such kind of document images hard to be recognized. Therefore it is necessary to restore warped document image before recognition. In this paper, a novel restore method is presented. The method takes a rough line segmentation and character segmentation firstly in order to estimate the warping direction. Then several pairs of key points mapping between the original image and the restored image are determined and thin-plate splines (TPS), which is an interpolation algorithm, is introduced to restore the image. Such process can effectively describe the warping direction of the document and successfully restore the image. Some experimental results show the effect of the image restoration and compare the recognition rate before and after the restoration based on a same OCR application.
34552917
A new dynamic Bayesian network for integrating multiple data in estimating gene networks
We propose a novel method for estimating a gene regulatory network from both gene expression data and transcription factor binding location data. Based on dynamic Bayesian network (DBN) models, our method has two advantages. First, structural expectation maximization algorithm is embedded into a DBN framework, allowing it to learn the network with unknown structure and incomplete data, which cannot be tackled by expectation maximization algorithm. Second, since learning only from gene expression data is not enough to accurately estimate a gene network, we incorporate transcription factor binding location data through a structure prior. We demonstrate the effectiveness of the proposed method by the analysis of Saccharomyces Cerevisiae cell cycle data. The experimental results show that this new approach suits for dealing with missing values. Furthermore, combination of heterogeneous data from multiple sources ensures that our results are more accurate than those recovered from gene expression data alone.
2479481
Gene Regulatory Network Construction Using Dynamic Bayesian Network (DBN) with Structure Expectation Maximization (SEM)
Discovering gene relationship from gene expression data is a hot topic in the post-genomic era. In recent years, Bayesian network has become a popular method to reconstruct the gene regulatory network due to the statistical nature. However, it is not suitable for analyzing the time-series data and cannot deal with cycles in the gene regulatory network. In this paper we apply the dynamic Bayesian network to model the gene relationship in order to overcome these difficulties. By incorporating the structural expectation maximization algorithm into the dynamic Bayesian network model, we develop a new method to learn the regulatory network from the S. Cerevisiae cell cycle gene expression data. The experimental results demonstrate that. the accuracy of our method outperforms the previous work.
41660705
Fault Diagnosis on Satellite Attitude Control with Dynamic Neural Network
With increasing demands on higher performance, more safety and reliability of dynamic systems, especially on safety-critical systems, fault diagnosis became a research interests in recent years. In this paper, a systematic approach to design fault diagnosis and accommodation with compound approach such as applying improved robust observer to fault diagnosis on linearized system aided by dynamic neural network, which is trained to bridge the gap between simulated system and real system on nonlinear attributes and modeling errors. Using an instance of fault diagnosis on attitude control system of satellite attitude, advantages of new scheme are tested to be effective.
4002740
Selectivity Estimation by Batch-Query based Histogram and Parametric Method
Histograms are used extensively for selectivity estimation and approximate query processing. Workload-aware dynamic histograms can self-tune itself based on query feedback without scanning or sampling the underlaying datasets in a systematic and comprehensive way. Dynamic histograms allocate more buckets not only for the areas with most skewed data distribution but also according to users' interest. However, it takes long time to 'warm-up' (i.e., a large number of queries need to be processed before the histogram can provide a satisfactory coverage and accuracy). Thus, it is less effective to adapt with workload pattern changes. In this paper, we propose a novel online query scheduling algorithm which can significantly reduce the warm-up time for dynamic histograms. A parametric method is proposed to remedy the problem of inaccurate query selectivity estimation for the areas with poor histogram coverage. Experimental results demonstrate a significant effectiveness and accuracy improvement of our approach.
5191068
Predicting the activity phase of a follower neuron with A-current in an inhibitory network
The transient potassium A-current is present in most neurons and plays an important role in determining the timing of action potentials. We examine the role of the A-current in the activity phase of a follower neuron in a rhythmic feed-forward inhibitory network with a reduced three-variable model and conduct experiments to verify the usefulness of our model. Using geometric analysis of dynamical systems, we explore the factors that determine the onset of activity in a follower neuron following release from inhibition. We first analyze the behavior of the follower neuron in a single cycle and find that the phase plane structure of the model can be used to predict the potential behaviors of the follower neuron following release from inhibition. We show that, depending on the relative scales of the inactivation time constant of the A-current and the time constant of the recovery variable, the follower neuron may or may not reach its active state following inhibition. Our simple model is used to derive a recursive set of equations to predict the contribution of the A-current parameters in determining the activity phase of a follower neuron as a function of the duration and frequency of the inhibitory input it receives. These equations can be used to demonstrate the dependence of activity phase on the period and duty cycle of the periodic inhibition, as seen by comparing the predictions of the model with the activity of the pyloric constrictor (PY) neurons in the crustacean pyloric network.
5001846
Propagation characteristics of wideband MIMO channel in urban micro- and macrocells
The wideband channel measurements at 580 MHz, 2.35 GHz and 4.90 GHz have been performed in the urban micro- and macrocell scenarios in the cities of China with multiple-input multiple-output (MIMO) channel sounder. The measured cases include line-of-sight (LOS) and non-line-of-sight (NLOS) propagation. Statistical results and comparative analysis for both scenarios are presented in this paper, including path loss (PL), root mean square (rms) delay spread (DS) and maximum excess delay (maxED), and angular spread (AS). In NLOS case, the frequency dependent factor (FDF) is observed as 32.1 by fitting the PL model of 2.35 GHz and 4.90 GHz. Moreover, a larger rms DS in urban microcell and AS at both base station (BS) and mobile subscriber (MS) are found for the denser and higher buildings in the cities of China. As the frequency varying from 580 MHz to 4.90 GHz, the median rms DS is decreased from 330 ns to 130 ns for NLOS case.
18794722
Word Sense Language Model for Information Retrieval
This paper proposes a word sense language model based method for information retrieval. This method, differing from most of traditional ones, combines word senses defined in a thesaurus with a classic statistical model. The word sense language model regards the word sense as a form of linguistic knowledge, which is helpful in handling mismatch caused by synonym and data sparseness due to data limit. Experimental results based on TREC-Mandarin corpus show that this method gains 12.5% improvement on MAP over traditional tf-idf retrieval method but 5.82% decrease on MAP compared to a classic language model. A combination result of this method and the language model yields 8.92% and 7.93% increases over either respectively. We present analysis and discussions on the not-so-exciting results and conclude that a higher performance of word sense language model will owe to high accurate of word sense labeling. We believe that linguistic knowledge such as word sense of a thesaurus will help IR improve ultimately in many ways.
5397904
Modeling and Throughput Analysis for Wireless Ad Hoc Networks
This paper presents a novel analytical model for wireless ad hoc networks. Based on the strong points in the literature, an accurate performance model for IEEE 802.11 MAC protocol is proposed. Moreover, by introducing the properties of spatial reuse, we extend the proposed model to support the throughput analysis of wireless ad hoc networks with directional antenna. An extensive set of simulations and numerical calculations validate our model by showing high accurate results.