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Reconstruction of viral population structure from next-generation sequencing data using multicommodity flows

BMC BIOINFORMATICS 2013年第Sup9期14卷 S2-S2页

作者： Skums, Pavel Mancuso, Nicholas Artyomenko, Alexander Tork, Bassam Mandoiu, Ion Khudyakov, Yury Zelikovsky, Alex Ctr Dis Control & Prevent Lab Mol Epidemiol & Bioinformat Div Viral Hepatitis Atlanta GA 30333 USA Georgia State Univ Dept Comp Sci Atlanta GA 30303 USA Univ Connecticut Dept Comp Sci & Engn Storrs CT 06269 USA

Background: Highly mutable RNA viruses exist in infected hosts as heterogeneous populations of genetically close variants known as quasispecies. Next-generation sequencing (NGS) allows for analysing a large number of viral sequences from infected patients, presenting a novel opportunity for studying the structure of a viral population and understanding virus evolution, drug resistance and immune escape. Accurate reconstruction of genetic composition of intra-host viral populations involves assembling the NGS short reads into whole-genome sequences and estimating frequencies of individual viral variants. Although a few approaches were developed for this task, accurate reconstruction of quasispecies populations remains greatly unresolved. Results: Two new methods, AmpMCF and ShotMCF, for reconstruction of the whole-genome intra-host viral variants and estimation of their frequencies were developed, based on Multicommodity Flows (MCFs). AmpMCF was designed for NGS reads obtained from individual PCR amplicons and ShotMCF for NGS shotgun reads. While AmpMCF, based on covering formulation, identifies a minimal set of quasispecies explaining all observed reads, ShotMCS, based on packing formulation, engages the maximal number of reads to generate the most probable set of quasispecies. Both methods were evaluated on simulated data in comparison to Maximum Bandwidth and ViSpA, previously developed state-of-the-art algorithms for estimating quasispecies spectra from the NGS amplicon and shotgun reads, respectively. Both algorithms were accurate in estimation of quasispecies frequencies, especially from large datasets. Conclusions: The problem of viral population reconstruction from amplicon or shotgun NGS reads was solved using the MCF formulation. The two methods, ShotMCF and AmpMCF, developed here afford accurate reconstruction of the structure of intra-host viral population from NGS reads. The implementations of the algorithms are available at http://***/vira.h

关键词： Root Mean Square Error Positive Predict Value expectation maximization expectation maximization algorithm Viral Population

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Genomic selection and complex trait prediction using a fast EM algorithm applied to genome-wide markers

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BMC BIOINFORMATICS 2010年第1期11卷 1-12页

作者： Shepherd, Ross K. Meuwissen, Theo H. E. Woolliams, John A. Cent Queensland Univ Sch Informat & Commun Technol Rockhampton Qld 4702 Australia Norwegian Univ Life Sci Inst Anim & Aquacultural Sci N-1432 As Norway Univ Edinburgh Royal Dick Sch Vet Studies Roslin EH25 9PS Midlothian Scotland Univ Edinburgh Roslin Inst Roslin EH25 9PS Midlothian Scotland

Background: The information provided by dense genome-wide markers using high throughput technology is of considerable potential in human disease studies and livestock breeding programs. Genome-wide association studies relate individual single nucleotide polymorphisms (SNP) from dense SNP panels to individual measurements of complex traits, with the underlying assumption being that any association is caused by linkage disequilibrium (LD) between SNP and quantitative trait loci (QTL) affecting the trait. Often SNP are in genomic regions of no trait variation. Whole genome Bayesian models are an effective way of incorporating this and other important prior information into modelling. However a full Bayesian analysis is often not feasible due to the large computational time involved. Results: This article proposes an expectation-maximization (EM) algorithm called emBayesB which allows only a proportion of SNP to be in LD with QTL and incorporates prior information about the distribution of SNP effects. The posterior probability of being in LD with at least one QTL is calculated for each SNP along with estimates of the hyperparameters for the mixture prior. A simulated example of genomic selection from an international workshop is used to demonstrate the features of the EM algorithm. The accuracy of prediction is comparable to a full Bayesian analysis but the EM algorithm is considerably faster. The EM algorithm was accurate in locating QTL which explained more than 1% of the total genetic variation. A computational algorithm for very large SNP panels is described. Conclusions: emBayesB is a fast and accurate EM algorithm for implementing genomic selection and predicting complex traits by mapping QTL in genome-wide dense SNP marker data. Its accuracy is similar to Bayesian methods but it takes only a fraction of the time.

关键词： Quantitative Trait Locus Single Nucleotide Polymorphism Lasso Genomic Selection expectation maximization algorithm

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A data-driven soft sensor based on weighted probabilistic slow feature analysis for nonlinear dynamic chemical processes

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JOURNAL OF CHEMOMETRICS 2023年第4期37卷 e3471-e3471页

作者： Zhang, Miao Xu, Beike Zhou, Le Zheng, Hui Jie, Jing Zhejiang Univ Sci & Technol Sch Automation & Elect Engn Hangzhou Peoples R China Zhejiang Univ Sci & Technol Sch Automation & Elect Engn Hangzhou 310023 Peoples R China

Modeling high-dimensional dynamic processes is a challenging task. In this regard, probabilistic slow feature analysis (PSFA) is revealed to be advantageous for dynamic soft sensor modeling, which can extract slowly varying intrinsic features from high-dimensional data. However, nonlinearities prevalent in industrial processes are not considered, which could lead to unsatisfactory prediction performance. In this paper, a weighted PSFA (WPSFA)-based soft sensor model is proposed for nonlinear dynamic chemical process. In WPSFA, a weighted log-likelihood function of complete data is constructed to linearize the nonlinear state emission equation. Then, the expectation maximization algorithm is applied to estimate the model parameters and a locally weighted regression model is established for quality prediction. Finally, the feasibility and effectiveness of the proposed approach are well illustrated through a numerical example and a real industrial process.

关键词： expectation maximization algorithm nonlinear dynamic processes soft sensor weighted probabilistic slow feature analysis

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Autoregressive double latent variables probabilistic model for higher-order dynamic process monitoring

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JOURNAL OF PROCESS CONTROL 2024年 141卷

作者： Ying, Ze Chang, Yuqing He, Yuchen Wang, Fuli Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Peoples R China Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang 110819 Peoples R China China Jiliang Univ Key Lab Intelligent Mfg Qual Big Data Tracing & An Hangzhou 310018 Peoples R China

The application of multivariate statistical analysis in process monitoring has emerged as a significant research topic, with a focus on consideration of data correlations. The present study investigates an anomaly detection method based on autoregressive double latent variables probabilistic (ADLVP) model for industrial dynamic processes. Specifically, the ADLVP model incorporates two distinct types of latent variables (LVs) to capture the internal relationships within the data from both quality-correlated and uncorrelated perspectives. Moreover, the model employs autoregressive modeling to characterize the double latent variables with time-dependence, enabling them to unveil more intricate higher-order autocorrelations between industrial measurements. The model parameters and the double latent variables can be iteratively determined using the expectation maximization (EM) algorithm, upon which the statistics for process monitoring are devised. Finally, the proposed method is validated in two industrial studies, and experimental results demonstrate that the ADLVP model outperforms its counterparts in dynamic processes monitoring.

关键词： Multivariate statistical analysis Dynamic process monitoring Probabilistic latent variable structure expectation maximization algorithm

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A Note on Parameters Estimation for Nonlinear Wiener Processes With Measurement Errors

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IEEE ACCESS 2019年 7卷 176756-176766页

作者： Tang, Shengjin Yu, Chuanqiang Sun, Xiaoyan Fan, Hongdong Si, Xiaosheng High Tech Inst Xian Xian 710025 Peoples R China

The nonlinear Wiener process has been widely used as a model for the degradation process. This note concerns parameters estimation of nonlinear Wiener processes with measurement errors (WPME) by the maximum likelihood estimation method. Firstly, we prove a rule that the estimated results based on the sample likelihood function developed through observations at each point are equal to the results from the first differences of the observations. This rule indicates that for reducing computation complexity the first differences of the observations may develop the sample likelihood function. Then we present a simple method to calculate the determinant and the inverse matrix of the covariance matrix of the WPME. This simple method could avoid the overflow error when calculating the determinant of the covariance matrix and the case that the inverse matrix is close to be singular, which could result in wrong estimation results. Secondly, we highlight the unit-specific assumption, which has a significant impact on parameters estimation but has been neglected in many papers. Then, we propose a modified expectation maximization algorithm for parameters estimation with random effects. Finally, to demonstrate the application and superiority of the proposed method, we provide a numerical example and a case study with comparison to several representative methods in the literature.

关键词： Degradation model maximum likelihood estimation Wiener process measurement error nonlinear expectation maximization algorithm

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Sparse Gaussian graphical mixture model

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Afrika Statistika 2016年第2期11卷 1041-1059页

作者： Lotsi ANANI Ernst WIT

This paper considers the problem of networks reconstruction from heterogeneous data using a Gaussian Graphical Mixture Model (GGMM). It is well known that parameter estimation in this context is challenging due to large numbers of variables coupled with the degenerate nature of the likelihood. We propose as a solution a penalized maximum likelihood technique by imposing an l1 penalty on the precision matrix. Our approach shrinks the parameters thereby resulting in better identifiability and variable selection. Cet article considère le probléme de la reconstruction de réseaux à partir de données hétérogènes en utilisant le modèle graphique gaussien mélangé (GGMM en Anglais). Il est connu que l'estimation paramétrique, dans ce contexte, n'est pas aisé à cause du grand nombre de variable et de la nature dégénérée de la vraisemblance. Nous proposons comme une solution une méthode de pénalisation du maximum de vraisemblance en imposant une pénalité de type l1 sur la précision de la matrice. Notre méthode réduit les paramétres et ainsi aboutit à une meilleure identification et à un meilleur choix des variables.

关键词： 62-09 62H12 62J07 expectation maximization algorithm Gaussian graphical mixture model graphical lasso

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

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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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