Bias Random Walk Research Articles

Network Embedding the Protein-Protein Interaction Network for Human Essential Genes Identification.

Essential genes are a group of genes that are indispensable for cell survival and cell fertility. Studying human essential genes helps scientists reveal the underlying biological mechanisms of a human cell but also guides disease treatment. Recently, the publication of human essential gene data makes it possible for researchers to train a machine-learning classifier by using some features of the known human essential genes and to use the classifier to predict new human essential genes. Previous studies have found that the essentiality of genes closely relates to their properties in the protein–protein interaction (PPI) network. In this work, we propose a novel supervised method to predict human essential genes by network embedding the PPI network. Our approach implements a bias random walk on the network to get the node network context. Then, the node pairs are input into an artificial neural network to learn their representation vectors that maximally preserves network structure and the properties of the nodes in the network. Finally, the features are put into an SVM classifier to predict human essential genes. The prediction results on two human PPI networks show that our method achieves better performance than those that refer to either genes’ sequence information or genes’ centrality properties in the network as input features. Moreover, it also outperforms the methods that represent the PPI network by other previous approaches.

Drug Side-Effect Prediction Via Random Walk on the Signed Heterogeneous Drug Network.

Drug side-effects have become a major public health concern as they are the underlying cause of over a million serious injuries and deaths each year. Therefore, it is of critical importance to detect side-effects as early as possible. Existing computational methods mainly utilize the drug chemical profile and the drug biological profile to predict the side-effects of a drug. In the utilized drug biological profile information, they only focus on drug–target interactions and neglect the modes of action of drugs on target proteins. In this paper, we develop a new method for predicting potential side-effects of drugs based on more comprehensive drug information in which the modes of action of drugs on target proteins are integrated. Drug information of multiple types is modeled as a signed heterogeneous information network. We propose a signed heterogeneous information network embedding framework for learning drug embeddings and predicting side-effects of drugs. We use two bias random walk procedures to obtain drug sequences and train a Skip-gram model to learn drug embeddings. We experimentally demonstrate the performance of the proposed method by comparison with state-of-the-art methods. Furthermore, the results of a case study support our hypothesis that modes of action of drugs on target proteins are meaningful in side-effect prediction.

Sparse network embedding for community detection and sign prediction in signed social networks

Network embedding is an important pre-process for analysing large scale information networks. Several network embedding algorithms have been proposed for unsigned social networks. However, these methods cannot be simply migrate to signed social networks which have both positive and negative relationships. In this paper, we present our signed social network embedding model which is based on the word embedding model. To deal with two kinds of links, we define two relationships: neighbour relationship and common neighbour relationship, as well as design a bias random walk procedure. In order to further improve interpretation of the representation vectors, the follow-proximally-regularized-leader online learning algorithm is introduced to the traditional word embedding framework to acquire sparse representations. Extensive experiments were carried out to compare our algorithm with three state-of-the-art methods for community detection and sign prediction tasks. The experimental results demonstrate that our algorithm performs better than the comparison algorithms on most signed social networks.

A New Filtering and Smoothing Algorithm for Railway Track Surveying Based on Landmark and IMU/Odometer.

High-accuracy railway track surveying is essential for railway construction and maintenance. The traditional approaches based on total station equipment are not efficient enough since high precision surveying frequently needs static measurements. This paper proposes a new filtering and smoothing algorithm based on the IMU/odometer and landmarks integration for the railway track surveying. In order to overcome the difficulty of estimating too many error parameters with too few landmark observations, a new model with completely observable error states is established by combining error terms of the system. Based on covariance analysis, the analytical relationship between the railway track surveying accuracy requirements and equivalent gyro drifts including bias instability and random walk noise are established. Experiment results show that the accuracy of the new filtering and smoothing algorithm for railway track surveying can reach 1 mm (1σ) when using a Ring Laser Gyroscope (RLG)-based Inertial Measurement Unit (IMU) with gyro bias instability of 0.03°/h and random walk noise of 0.005 while control points of the track control network (CPIII) position observations are provided by the optical total station in about every 60 m interval. The proposed approach can satisfy at the same time the demands of high accuracy and work efficiency for railway track surveying.

Skew redundant MEMS IMU calibration using a Kalman filter

In this paper, a novel calibration procedure for skew redundant inertial measurement units (SRIMUs) based on micro-electro mechanical systems (MEMS) is proposed. A general model of the SRIMU measurements is derived which contains the effects of bias, scale factor error and misalignments. For more accuracy, the effect of lever arms of the accelerometers to the center of the table are modeled and compensated in the calibration procedure. Two separate Kalman filters (KFs) are proposed to perform the estimation of error parameters for gyroscopes and accelerometers. The predictive error minimization (PEM) stochastic modeling method is used to simultaneously model the effect of bias instability and random walk noise on the calibration Kalman filters to diminish the biased estimations. The proposed procedure is simulated numerically and has expected experimental results. The calibration maneuvers are applied using a two-axis angle turntable in a way that the persistency of excitation (PE) condition for parameter estimation is met. For this purpose, a trapezoidal calibration profile is utilized to excite different deterministic error parameters of the accelerometers and a pulse profile is used for the gyroscopes. Furthermore, to evaluate the performance of the proposed KF calibration method, a conventional least squares (LS) calibration procedure is derived for the SRIMUs and the simulation and experimental results compare the functionality of the two proposed methods with each other.

Effect of Carouseling on Angular Rate Sensor Error Processes

Carouseling is an efficient method to mitigate the measurement errors of inertial sensors, particularly MEMS gyroscopes. In this article, the effect of carouseling on the most significant stochastic error processes of a MEMS gyroscope, i.e., additive bias, white noise, 1/f noise, and rate random walk, is investigated. Variance propagation equations for these processes under averaging and carouseling are defined. Furthermore, a novel approach to generating 1/f noise is presented. The experimental results show that carouseling reduces the contributions of additive bias, 1/f noise, and rate random walk significantly in comparison with plain averaging, which can be utilized to improve the accuracy of dead reckoning systems.

PEM Stochastic Modeling for MEMS Inertial Sensors in Conventional and Redundant IMUs

Prediction error minimization (PEM) method is used for modeling microelectromechanical (MEMS) system-based inertial sensor stochastic errors instead of autoregressive (AR) processes that are implemented in most of the recent studies. Since the sensor outputs are buried in a high-power white noise, the linear prediction methods used in the AR approach for bias instability modeling leads to biased estimates in parameter identification. Wavelet multiresolution techniques have been suggested so far for signal denoising to attenuate the effect of high-power white noise. However, the main sensor signal as well as the bias instability dynamics may be distorted during this procedure. The PEM method prepares the possibility to simultaneously model the bias instability and random walk as the most effective components of the MEMS inertial sensor's residual error. Therefore, the proposed method can be an alternative to the combination of the AR processes and wavelet decomposition in inertial sensor's stochastic error modeling. The performance of the proposed approach is illustrated in terms of prediction error indexes. Furthermore, the effectiveness of the modeling approach is shown in a static 2-D navigation application aided by zero velocity updates. The experimental results from real sensor's static data for both an inertial measurement unit (IMU) with conventional structure as well as a skew redundant IMU (SRIMU) show that the proposed PEM approach outperforms the AR method. The advantage of the method is more apparent for the SRIMU. The well-known $t$ -test statistical testing is exploited to guarantee the confidence of the results.

Effect of Mixing Assortativity on Extreme Events in Complex Networks

We investigate the effect of mixing assortativity on the occurrence of extreme events in complex networks. The bias random walk model is adopted with a preferential transition probability tuned by a parameter α. We derive exact expressions for the stationary distribution probability and for the occurrence probability of extreme events. They reveal that the occurrence of extreme events strongly depends on the mixing assortativity of the network. It is shown that, for non-assortative (rt = 0), assortative (rt = 0.15) and disassortative (rt = −0.15) scale-free networks, the minimal occurrence of extreme events will happen at α = −1.0, α = −0.6 andα = 0.2, respectively.

A multi-frequency signal processing method for fiber-optic gyroscopes with square wave modulation

The bias stability and random walk coefficients (RWC) of interferometric fiber-optic gyroscopes (IFOGs) are directly affected by characteristic noises produced by optoelectronics interactions in optic sensors. This paper documents a novel demodulation method for square wave modulated IFOGs, a method capable of suppressing the white noise that results from optical intensity noises and circuit noises as well as shot noises. In addition, this paper provides a statistical analysis of IFOG signals. Through use of orthogonal harmonic demodulation followed by deployment of matched filters to detract the Sagnac phase from the IFOGs, these channels we then processed, using principle component analysis (PCA), to establish optimal independent synchronous quadrature signal channels. Finally a difference procedure was carried out for the outputs. Our results showed that an experimental sample of the proposed IFOG (1982 m coil under uncontrolled room temperature) achieved a real-time output variance improvement in detecting the Earth's rotation rate, which is well matched with theoretical calculations of its Cramèr-Rao bound (CRB).