Prediction of primate splice site using inhomogeneous Markov chain and neural network

The inhomogeneous Markov chain model is used to discriminate acceptor and donor sites in genomic DNA sequences. It outperforms statistical methods such as homogeneous Markov chain model, higher order Markov chain and interpolated Markov chain models, and machine-learning methods such as k-nearest neighbor and support vector machine as well. Besides its high accuracy, another advantage of inhomogeneous Markov chain model is its simplicity in computation. In the three states system (acceptor, donor, and neither), the inhomogeneous Markov chain model is combined with a three-layer feed forward neural network. Using this combined system 3175 primate splice-junction gene sequences have been tested, with a prediction accuracy of greater than 98%.     

土地利用/景观生态学研究中的马尔可夫链统计性质分析

马尔可夫链在土地利用和景观生态学研究中得到了广泛应用,而应用中通常假设土地利用变化为满足马尔可夫性的一阶时齐马尔可夫链,对马尔可夫链的统计性质是否成立却很少进行检验.本文以北京市土地利用变化监测数据为算例,提出了马尔可夫链统计性质的皮尔逊χ² 拟合优度检验方法.检验结果表明,土地利用研究中通常假设的时齐性和马尔可夫性(一阶性)在统计学上并不成立,即北京土地利用演变过程为非时齐的高阶马尔可夫链.相对于马尔可夫统计性质的似然比检验中转移概率大于零的要求,皮尔逊χ²检验对转移概率的要求相对宽松,允许转移概率为零,所以应用的范围较似然比检验更为广泛.     

A Markov chain Monte Carlo strategy for sampling from the joint posterior distribution of pedigrees and population parameters under a Fisher-Wright model with partial selfing

A simple population genetic model is presented for a hermaphrodite annual species, allowing both selfing and outcrossing. Those male gametes (pollen) responsible for outcrossing are assumed to disperse much further than seeds. Under this model, the pedigree of a sample from a single locality is loop-free. A novel Markov chain Monte Carlo strategy is presented for sampling from the joint posterior distribution of the pedigree of such a sample and the parameters of the population genetic model (including the selfing rate) given the genotypes of the sampled individuals at unlinked marker loci. The computational costs of this Markov chain Monte Carlo strategy scale well with the number of individuals in the sample, and the number of marker loci, but increase exponentially with the age (time since colonisation from the source population) of the local population. Consequently, this strategy is particularly suited to situations where the sample has been collected from a population which is the result of a recent colonisation process.     

On the distribution of the nucleotides in seven completely sequenced DNAs

Several Markov chain models (up to fourth order) have been fitted to the sequences of the seven DNAs presented in Fuchs et al. (1980). Two methods for determining the order of Markov chain are applied to the data. The two methods lead to different conclusions and we dicuss these discrepancies. When the distribution of the nucleotides in a DNA sequence is investigated, it is suggested that the study on the order of the Markov model should be supplemented with additional analysis.     

On the distribution of the nucleotides in the seven completely sequenced DNAs

Several Markov chain models (up to fourth order) have been fitted to the sequences of the seven DNAs presented in Fuchs et al. (1980). Two methods for determining the order of Markov chain are applied to the data. The two methods lead to different conclusions and we dicuss these discrepancies. When the distribution of the nucleotides in a DNA sequence is investigated, it is suggested that the study on the order of the Markov model should be supplemented with additional analysis.     

Bayesian restoration of a hidden Markov chain with applications to DNA sequencing.

Hidden Markov models (HMMs) are a class of stochastic models that have proven to be powerful tools for the analysis of molecular sequence data. A hidden Markov model can be viewed as a black box that generates sequences of observations. The unobservable internal state of the box is stochastic and is determined by a finite state Markov chain. The observable output is stochastic with distribution determined by the state of the hidden Markov chain. We present a Bayesian solution to the problem of restoring the sequence of states visited by the hidden Markov chain from a given sequence of observed outputs. Our approach is based on a Monte Carlo Markov chain algorithm that allows us to draw samples from the full posterior distribution of the hidden Markov chain paths. The problem of estimating the probability of individual paths and the associated Monte Carlo error of these estimates is addressed. The method is illustrated by considering a problem of DNA sequence multiple alignment. The special structure for the hidden Markov model used in the sequence alignment problem is considered in detail. In conclusion, we discuss certain interesting aspects of biological sequence alignments that become accessible through the Bayesian approach to HMM restoration.     

A Full Bayesian Approach for Boolean Genetic Network Inference

Boolean networks are a simple but efficient model for describing gene regulatory systems. A number of algorithms have been proposed to infer Boolean networks. However, these methods do not take full consideration of the effects of noise and model uncertainty. In this paper, we propose a full Bayesian approach to infer Boolean genetic networks. Markov chain Monte Carlo algorithms are used to obtain the posterior samples of both the network structure and the related parameters. In addition to regular link addition and removal moves, which can guarantee the irreducibility of the Markov chain for traversing the whole network space, carefully constructed mixture proposals are used to improve the Markov chain Monte Carlo convergence. Both simulations and a real application on cell-cycle data show that our method is more powerful than existing methods for the inference of both the topology and logic relations of the Boolean network from observed data.     

Note on the spread of a state in small social groups

A model is developed for the spread of a state in small social groups. Under suitable assumptions the model exhibits formal identity with Markov chain theory. The basic theorems and classifications of Markov chain theory are stated and interpreted in terms of the model. Finally, some procedures for testing the model are indicated.     

Analysis of Longitudinal Data of Epileptic Seizure Counts – A Two‐State Hidden Markov Regression Approach

This paper discusses a two‐state hidden Markov Poisson regression (MPR) model for analyzing longitudinal data of epileptic seizure counts, which allows for the rate of the Poisson process to depend on covariates through an exponential link function and to change according to the states of a two‐state Markov chain with its transition probabilities associated with covariates through a logit link function. This paper also considers a two‐state hidden Markov negative binomial regression (MNBR) model, as an alternative, by using the negative binomial instead of Poisson distribution in the proposed MPR model when there exists extra‐Poisson variation conditional on the states of the Markov chain. The two proposed models in this paper relax the stationary requirement of the Markov chain, allow for overdispersion relative to the usual Poisson regression model and for correlation between repeated observations. The proposed methodology provides a plausible analysis for the longitudinal data of epileptic seizure counts, and the MNBR model fits the data much better than the MPR model. Maximum likelihood estimation using the EM and quasi‐Newton algorithms is discussed. A Monte Carlo study for the proposed MPR model investigates the reliability of the estimation method, the choice of probabilities for the initial states of the Markov chain, and some finite sample behaviors of the maximum likelihood estimates, suggesting that (1) the estimation method is accurate and reliable as long as the total number of observations is reasonably large, and (2) the choice of probabilities for the initial states of the Markov process has little impact on the parameter estimates.     

StatAlign: an extendable software package for joint Bayesian estimation of alignments and evolutionary trees

MOTIVATION: Bayesian analysis is one of the most popular methods in phylogenetic inference. The most commonly used methods fix a single multiple alignment and consider only substitutions as phylogenetically informative mutations, though alignments and phylogenies should be inferred jointly as insertions and deletions also carry informative signals. Methods addressing these issues have been developed only recently and there has not been so far a user-friendly program with a graphical interface that implements these methods. RESULTS: We have developed an extendable software package in the Java programming language that samples from the joint posterior distribution of phylogenies, alignments and evolutionary parameters by applying the Markov chain Monte Carlo method. The package also offers tools for efficient on-the-fly summarization of the results. It has a graphical interface to configure, start and supervise the analysis, to track the status of the Markov chain and to save the results. The background model for insertions and deletions can be combined with any substitution model. It is easy to add new substitution models to the software package as plugins. The samples from the Markov chain can be summarized in several ways, and new postprocessing plugins may also be installed.     

Detecting Memory and Structure in Human Navigation Patterns Using Markov Chain Models of Varying Order

One of the most frequently used models for understanding human navigation on the Web is the Markov chain model, where Web pages are represented as states and hyperlinks as probabilities of navigating from one page to another. Predominantly, human navigation on the Web has been thought to satisfy the memoryless Markov property stating that the next page a user visits only depends on her current page and not on previously visited ones. This idea has found its way in numerous applications such as Google''s PageRank algorithm and others. Recently, new studies suggested that human navigation may better be modeled using higher order Markov chain models, i.e., the next page depends on a longer history of past clicks. Yet, this finding is preliminary and does not account for the higher complexity of higher order Markov chain models which is why the memoryless model is still widely used. In this work we thoroughly present a diverse array of advanced inference methods for determining the appropriate Markov chain order. We highlight strengths and weaknesses of each method and apply them for investigating memory and structure of human navigation on the Web. Our experiments reveal that the complexity of higher order models grows faster than their utility, and thus we confirm that the memoryless model represents a quite practical model for human navigation on a page level. However, when we expand our analysis to a topical level, where we abstract away from specific page transitions to transitions between topics, we find that the memoryless assumption is violated and specific regularities can be observed. We report results from experiments with two types of navigational datasets (goal-oriented vs. free form) and observe interesting structural differences that make a strong argument for more contextual studies of human navigation in future work.     

Comparing vector–host and SIR models for dengue transmission

Various simple mathematical models have been used to investigate dengue transmission. Some of these models explicitly model the mosquito population, while others model the mosquitoes implicitly in the transmission term. We study the impact of modeling assumptions on the dynamics of dengue in Thailand by fitting dengue hemorrhagic fever (DHF) data to simple vector–host and SIR models using Bayesian Markov chain Monte Carlo estimation. The parameter estimates obtained for both models were consistent with previous studies. Most importantly, model selection found that the SIR model was substantially better than the vector–host model for the DHF data from Thailand. Therefore, explicitly incorporating the mosquito population may not be necessary in modeling dengue transmission for some populations.     

Hidden Markov Models and Animal Behaviour

This paper proposes the use of hidden Markov time series models for the analysis of the behaviour sequences of one or more animals under observation. These models have advantages over the Markov chain models commonly used for behaviour sequences, as they can allow for time-trend or expansion to several subjects without sacrificing parsimony. Furthermore, they provide an alternative to higher-order Markov chain models if a first-order Markov chain is unsatisfactory as a model. To illustrate the use of such models, we fit multivariate and univariate hidden Markov models allowing for time-trend to data from an experiment investigating the effects of feeding on the locomotory behaviour of locusts (Locusta migratoria).     

Bayesian analysis of discrete survival data with a hidden Markov chain

This paper considers the discrete survival data from a Bayesian point of view. A sequence of the baseline hazard functions, which plays an important role in the discrete hazard function, is modeled with a hidden Markov chain. It is explained how the resultant model is implemented via Markov chain Monte Carlo methods. The model is illustrated by an application of real data.     

修正非齐次马尔可夫模型应用的研究

修正非齐次模型是在齐次模型和非齐次模型基础上提出的适用于蛋白质编码区的马尔可夫模型。此模型可以用来分析生物物种进化和基因突变,模型中的马尔可夫度与序列进化水平相关联,转移矩阵与基因突变相关联。本文通过比较7类不同物种-1度马尔可夫链的含量,验证了生物物种进化反映在密码子使用上的特征;通过密码子位点间转移矩阵的计算,分析了基因突变在密码子不同位点上发生的可能性。     

Statistical inference and model selection for the 1861 Hagelloch measles epidemic

A stochastic epidemic model is proposed which incorporates heterogeneity in the spread of a disease through a population. In particular, three factors are considered: the spatial location of an individual's home and the household and school class to which the individual belongs. The model is applied to an extremely informative measles data set and the model is compared with nested models, which incorporate some, but not all, of the aforementioned factors. A reversible jump Markov chain Monte Carlo algorithm is then introduced which assists in selecting the most appropriate model to fit the data.     

Architectural analysis and modelling of the branching process of the young oil-palm root system

An architectural analysis of the root system of young oil-palm (Elaeis guineensis Jacq.) seedlings was made. In this analysis, root branching was modelled by a Markov chain (discrete-time, discrete-state space stochastic process). This study has been realized on radicles of young oil-palm seedlings which were considered as main axes which branch. We defined an elementary length unit as the smallest length between two successive lateral roots. The model was based on the analysis of a sequence of events, each event being indexed by the rank of the elementary length unit on the main axis. An event was defined as the state of the length unit, chosen between unbranched state and three branched-state categories. The branching process of the oil-palm radicle was modelled by a four-state first-order Markov chain. Consequently, the state of an elementary length unit depended only on the state of the previous one. The Markov chain was homogeneous, i.e. the transition probabilities did not depend on the rank of the elementary length unit.This study allowed us to identify a probabilistic model of root branching which was the first step in the elaboration of a stochastic model of the architecture of the oil-palm root system.     

Reversible jump Markov chain Monte Carlo computation and Bayesian model determination

Markov chain Monte Carlo methods for Bayesian computation haveuntil recently been restricted to problems where the joint distributionof all variables has a density with respect to some fixed standardunderlying measure. They have therefore not been available forapplication to Bayesian model determination, where the dimensionalityof the parameter vector is typically not fixed. This paper proposesa new framework for the construction of reversible Markov chainsamplers that jump between parameter subspaces of differingdimensionality, which is flexible and entirely constructive.It should therefore have wide applicability in model determinationproblems. The methodology is illustrated with applications tomultiple change-point analysis in one and two dimensions, andto a Bayesian comparison of binomial experiments.     

基于马尔科夫链的医疗决策

在临床实践中,医生和患者均面临决策,由于医生和患者个体知识经验的局限性,仅依赖个人经验的决策判断难以全面评估治疗方案的好坏,而通过马尔科夫链模型可以帮助医生和患者对复杂疾病建立抽象模型,便于对疾病的各治疗效果进行决策分析。马尔科夫链模型是处理离散事件的随机过程,通过当前设定的信息,预测将来的情况。本文总结了马尔科夫链在医疗决策中的应用的基本原理,梳理了在医疗决策领域常用的马尔科夫链模型的分类,针对医疗决策的特点探讨不同类型马尔科夫链的矩阵法、队列法以及蒙特卡洛模拟分析方法的适用范围和优缺点。针对疾病进展的三状态模型以及是否使用某药物的实际决策案例,分析比较队列法与蒙特卡洛模拟法的具体应用,总结归纳队列法与蒙特卡洛模拟法的优缺点。     

A Bayesian approach to DNA sequence segmentation

Many deoxyribonucleic acid (DNA) sequences display compositional heterogeneity in the form of segments of similar structure. This article describes a Bayesian method that identifies such segments by using a Markov chain governed by a hidden Markov model. Markov chain Monte Carlo (MCMC) techniques are employed to compute all posterior quantities of interest and, in particular, allow inferences to be made regarding the number of segment types and the order of Markov dependence in the DNA sequence. The method is applied to the segmentation of the bacteriophage lambda genome, a common benchmark sequence used for the comparison of statistical segmentation algorithms.