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Prediction of RNA Polymerase Binding Sites Using Purine-Pyrimidine Encoding and Hybrid Learning Methods

International Journal of Applied Science and Engineering 2004年第2期2卷 177-188页

作者： Cheng-Jian Lin Chun-Cheng Peng and Chi-Yung Lee Department of Computer Science and Information Engineering Chaoyang University of Technology bSchool of Computer Science and Information Systems University of London Department of Electronic Engineering Nan Kai College

Escherichia coli (E. coli) K12 was sequenced in 1997. The 4,639,221-base pair DNA sequence consists of 4288 annotated protein-coding genes, 38 percent of which have no attrib- uted function. One of the major problems in predicting prokaryotic promoters is locating the spacers between the -35 box and -10 box and between the -10 box and transcription start site. In this paper, we use the adopted expectation maximization (EM) algorithm to accurately find the localizations of the promoter regions. A brand new purine-pyrimidine encoding method is pro- posed to reduce the dimensions of the training data. The heavy demand on systems for both computation and memory space can then be avoided through the choice of coding factor. The most representative features are used for training learning vector quantization networks. The simulation results of the proposed coding approach reveal that the precision of promoter predic- tion using the proposed approach is approximately the same as the precision using the traditional encoding method.

关键词： E. coli promoter prediction purine-pyrimidine expectation maximization algorithm learning vector quantization networks.

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Remaining useful life re-prediction methodology based on Wiener process: Subsea Christmas tree system as a case study

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COMPUTERS & INDUSTRIAL ENGINEERING 2021年 151卷 106983-106983页

作者： Cai, Baoping Fan, Hongyan Shao, Xiaoyan Liu, Yonghong Liu, Guijie Liu, Zengkai Ji, Renjie China Univ Petr Natl Engn Lab Offshore Geophys & Explorat Equipme Qingdao 266580 Shandong Peoples R China China Univ Petr Coll Mech & Elect Engn Qingdao 266580 Shandong Peoples R China Ocean Univ China Dept Mech & Elect Engn Qingdao 266100 Shandong Peoples R China

With the continuous improvement of the complexity and comprehensive level of the system, its reliability becomes more and more important. The remaining useful life (RUL) estimation method using the degradation model with random effect to describe the degradation process of the system has been widely used such as Wiener process. However, the conventional Wiener-process-based degradation model only considers the current monitoring data but not the historical degradation data, which leads to the inaccuracy of RUL prediction. Furthermore, in engineering, there will always be data missing caused by sensor networks, long life cycle properties of system and so on, leading to unsatisfactory results. This paper contributed a RUL re-prediction method based on Wiener process combining the current monitoring status and historical degradation data of the system. In the initial prediction process, the Wiener process is used to describe the degradation process of the system, the drift coefficient and diffusion coefficient are estimated by expectation maximization algorithm (EM algorithm), and the dynamic Bayesian networks (DBNs) model for system performance degradation is established to solve the uncertainty caused by missing data. In the re-prediction process, n groups of performance degradation monitoring data and historical predicted data are combined to calculate the basic degradation in each stage of Wiener process, and the DBNs are used for modeling. The RUL value is obtained by the time difference between the detection point and the predicted fault point, it is determined by the failure threshold finally. A case of subsea Christmas tree system is adopted to demonstrate the proposed approach.

关键词： Remaining useful life Wiener process Dynamic Bayesian networks expectation maximization algorithm Subsea Christmas tree system

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Probabilistic principal component analysis for metabolomic data

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BMC BIOINFORMATICS 2010年第1期11卷 571-571页

作者： Nyamundanda, Gift Brennan, Lorraine Gormley, Isobel Claire Univ Coll Dublin Sch Math Sci Dublin Ireland Univ Coll Dublin Conway Inst Sch Agr Food Sci & Vet Med Dublin Ireland

Background: Data from metabolomic studies are typically complex and high-dimensional. Principal component analysis (PCA) is currently the most widely used statistical technique for analyzing metabolomic data. However, PCA is limited by the fact that it is not based on a statistical model. Results: Here, probabilistic principal component analysis (PPCA) which addresses some of the limitations of PCA, is reviewed and extended. A novel extension of PPCA, called probabilistic principal component and covariates analysis (PPCCA), is introduced which provides a flexible approach to jointly model metabolomic data and additional covariate information. The use of a mixture of PPCA models for discovering the number of inherent groups in metabolomic data is demonstrated. The jackknife technique is employed to construct confidence intervals for estimated model parameters throughout. The optimal number of principal components is determined through the use of the Bayesian Information Criterion model selection tool, which is modified to address the high dimensionality of the data. Conclusions: The methods presented are illustrated through an application to metabolomic data sets. Jointly modeling metabolomic data and covariates was successfully achieved and has the potential to provide deeper insight to the underlying data structure. Examination of confidence intervals for the model parameters, such as loadings, allows for principled and clear interpretation of the underlying data structure. A software package called MetabolAnalyze, freely available through the R statistical software, has been developed to facilitate implementation of the presented methods in the metabolomics field.

关键词： Principal Component Analysis Bayesian Information Criterion expectation maximization algorithm Metabolomic Data Loading Matrix

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Nonlinear system identification with multiple and correlated scheduling variables *

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IFAC Proceedings Volumes 2013年第32期46卷 319-324页

作者： Lei Chen Biao Huang Fei Liu Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education) Institute of Automation Jiangnan University Wuxi 214122 P.R. China Department of Chemical and Materials Engineering University of Alberta Edmonton AB T6G 2G6 Canada

This paper is concerned with identification of nonlinear systems with multiple and correlated scheduling variables. Multiple auto regressive exogenous (ARX) models are identified on different process operating conditions, and a normalized exponential function as the probability density function associated with each of the local ARX models taking effect is then used to combine all the local models to represent the complete dynamics of a nonlinear system. The parameters of the local ARX models and the exponential functions are estimated simultaneously under the framework of the expectation maximization (EM) algorithm. A numerical example is applied to demonstrate the proposed identification method.

关键词： System identification Nonlinear process Multiple models expectation maximization algorithm Multiple scheduling variables

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Parametric Pulse Train De-Interleaving of Stochastic Sources

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IFAC Proceedings Volumes 1996年第1期29卷 4231-4236页

作者： Andrew Logothetis Vikram Krishnamurthy H. Vincent Poor CRC for Sensor Signal and Information Processing Department of Electrical and Electronic Engineering University of Melbourne Parkville Victoria 3052 Australia Department of Electrical Engineering Princeton University Princeton NJ 08544-5263 USA

In this paper we consider de-interleaving a finite number of stochastic parametric sources. The sources are modeled as independent autoregressive (AR) processes. Based on a Markovian switching policy, we assume that the different sources transmit signals on the same single channel. The receiver records the 1-bit quantized version of the transmitted signal and aims to identify the sequence of active sources. Once the source sequence has been identified, the characteristics (parameters) of each source is estimated.

关键词： Autoregressive Models Parameter Estimation expectation maximization algorithm Hidden Markov Models Binary Time Series

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Multi-model approach to nonlinear system identification with unknown time delay

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IFAC Proceedings Volumes 2014年第3期47卷 9388-9393页

This paper is concerned with identification of nonlinear systems with a noisy scheduling variable, and the measurement of the system has an unknown time delay. Auto regressive exogenous (ARX) models are selected as the local models, and multiple local models are identified along the process operating points. The dynamics of a nonlinear system are represented by associating a normalized exponential function with each of the ARX models; therein, the normalized exponential function is acted as the probability density function. The parameters of the ARX models and the exponential functions as well as the unknown time delay are estimated simultaneously under the expectation maximization (EM) algorithm using the retarded input-output data. A CSTR example is given to verify the proposed identification approach.

关键词： Nonlinear system identification expectation maximization algorithm Multiple models Time delay

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System Identification from Multi-Rate Data

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IFAC Proceedings Volumes 2004年第1期37卷 155-160页

作者： R. Bhushan Gopaluni Harigopal Raghavan Sirish L. Shah Department of Chemical & Materials Engineering University of Alberta Edmonton AB CANADA - T6G 2G6

In this paper, we provide a novel iterative identification algorithm for multi-rate sampled data systems. The procedure involves, as a first step, identifying a simple initial model from multi-rate data. Based on this model, the "missing" data points in the slow sampled measurements are estimated following the expectation maximization approach. Using the estimated missing data points and the original data set, a new model is obtained and this procedure is repeated until the models converge. An attractive feature of the proposed method lies in its applicability to irregularly sampled data. An application of the proposed method to an industrial data set is also included.

关键词： identification multi-rate processes expectation maximization algorithm

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Nonlinear budget set regressions in random utility models: Theory and application to taxable income

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Journal of Econometrics 2024年

作者： Blomquist, Soren Kumar, Anil Liang, Che-Yuan Newey, Whitney K. Uppsala Center for Fiscal studies Department of Economics Uppsala University Sweden Department of Economics University of Iowa United States MIT Department of Economics United States NBER United States

This paper is about the nonparametric regression of a choice variable on a nonlinear budget set under utility maximization with general heterogeneity, i.e. in the random utility model (RUM). We show that utility maximization and convex budget sets make this regression three dimensional with a more parsimonious specification than previously derived. We show that nonconvexities in the budget set will have little effect on these results in important cases. We characterize all the restrictions of utility maximization on the budget set regression and show how to check these restrictions in applications. We formulate budget set effects that can be identified by this regression and give automatic debiased machine learners of these effects. We consider use of control functions to allow for endogeneity. Throughout we take as the main example the effect of taxes on taxable income including accounting for productivity growth. In an application to Swedish data we find the taxable income elasticity of a change in the slope of each segment to be .52, that the regression satisfies the restrictions of utility maximization at the values chosen for over 95% of observations, and that a productivity growth rate we estimate is close to other estimates. © 2024 Elsevier B.V.

关键词： expectation maximization algorithm

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AGSEI: Adaptive Graph Structure Estimation with Long-Tail Distributed Implicit Graphs

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IEEE Transactions on Emerging Topics in Computing 2024年

作者： He, Yunfei Wu, Yang Huang, Lishan Peng, Zhenwan Yang, Fei Zhang, Yiwen Sheng, Victor S Anhui Medial University School of Biomedical Engineering China Anhui University School of Computer Science and Technology China Texas Tech University School of Department of Computer Science United States

Empowered by their remarkable advantages, graph neural networks (GNN) serve as potent tools for embedding graph-structured data and finding applications across various domains. Particularly, a prevalent assumption in most GNNs is the reliability of the underlying graph structure. This assumption, often implicit, can inadvertently lead to the propagation of misleading information through structures like false links. In response to this challenge, numerous methods for graph structure learning (GSL) have been developed. Among these methods, one popular approach is to construct a simple and intuitive K-nearest neighbor (KNN) graph as a sample to infer true graph structure. However, KNN graphs that follow the single-point distribution can easily mislead the true graph structure estimation. The primary reason is that, from a statistical perspective, the KNN graph, as a sample, follows a single-point distribution, whereas the true graph structure, as the population, as a whole mostly follows a long-tail distribution. In theory, the sample and the population should share the same distribution;otherwise, accurately inferring the true graph structure becomes challenging. To address this problem, this paper proposes an Adaptive Graph Structure Estimation with Long-Tail Distributed Implicit Graph, referred to as AGSEI. AGSEI comprises three main components: long-tail implicit graph construction, explicit graph structure estimation, and joint optimization. The first component relies on a multi-layer graph convolutional network to learn low-order to high-order node representations, compute node similarity, and construct several corresponding long-tail implicit graphs. Since the original imperfect graph structure can mislead GNNs into propagating false information, it reduces the reliability of the long-tail implicit graphs. AGSEI attempts to limit the aggregation of irrelevant information by introducing the Hilbert-Schmidt independence criterion. That is, maximizing the dependenc

关键词： expectation maximization algorithm

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A novel transmission-based test of association for multivariate phenotypes: an application to systolic and diastolic blood pressure levels

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BMC proceedings 2014年第SUPPL 1期8卷 S71页

作者： Tanushree Haldar Indranil Mukhopadhyay Saurabh Ghosh Human Genetics Unit Indian Statistical Institute 203 B.T. Road Kolkata 700108 India.

Unlike case-control studies, family-based tests for association are protected against population stratification. Complex genetic traits are often governed by quantitative precursors and it has been argued that it may be a more powerful strategy to analyze these quantitative precursors instead of the clinical end point trait. Although methods have been developed for family-based association tests for single quantitative traits, it is of interest to develop such methods for multivariate phenotypes. We propose a novel transmission-based approach based on a trio design using a simple logistic regression to test for association with a multivariate phenotype. We use our proposed method to analyze data on systolic and diastolic blood pressure levels provided in Genetic Analysis Workshop 18. However, we find that the bivariate analysis of the two phenotypes did not provide more promising results compared to univariate analyses, suggesting a possibility of a different set of major genetic variants modulating the two phenotypes.

关键词： expectation maximization algorithm Genetic Analysis Workshop Transmission Disequilibrium Heterozygous Parent Systolic Blood Pressure Level

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