Order Markov Model Research Articles

Coupling of Conformational Switches in Calcium Sensor Unraveled with Local Markov Models and Transfer Entropy.

Proteins often have multiple switching domains that are coupled to each other and to the binding of ligands in order to realize signaling functions. Here we investigate the C2A domain of Synaptotagmin-1 (Syt-1), a calcium sensor in the neurotransmitter release machinery and a model system for the large family of C2 membrane binding domains. We combine extensive molecular dynamics (MD) simulations with Markov modeling in order to model conformational switching domains, their states, and their dependence on bound calcium ions. Then, we use transfer entropy to characterize how the switching domains are coupled via directed or allosteric mechanisms and give rise to the calcium sensing function of the protein. Our proposed switching mechanism contributes to the understanding of the neurotransmitter release machinery. Furthermore, the methodological approach we develop serves as a template to analyze conformational switching domains and the broad study of their coupling in macromolecular machines.

High-Order Entropy-Based Population Diversity Measures in the Traveling Salesman Problem

To maintain the population diversity of genetic algorithms (GAs), we are required to employ an appropriate population diversity measure. However, commonly used population diversity measures designed for permutation problems do not consider the dependencies between the variables of the individuals in the population. We propose three types of population diversity measures that address high-order dependencies between the variables to investigate the effectiveness of considering high-order dependencies. The first is formulated as the entropy of the probability distribution of individuals estimated from the population based on an -th--order Markov model. The second is an extension of the first. The third is similar to the first, but it is based on a variable order Markov model. The proposed population diversity measures are incorporated into the evaluation function of a GA for the traveling salesman problem to maintain population diversity. Experimental results demonstrate the effectiveness of the three types of high-order entropy-based population diversity measures against the commonly used population diversity measures.

Identification of regional dialects of Telugu language using text independent speech processing models

Telugu language is one of the important languages in the world. The language that is spoken by most of the people in a region is called as dialect. In the recent days, speech recognition system is present in almost all electronic devices. In this, dialects of particular language perform a vital role. The accurate dialects identification technique helps in not only enhancing its features but also expected to provide in modern services in health and telemedicine for older and homebound peoples. Like any other language, even Telugu language has diversified itself into different dialects viz., Telangana, Kostha Andhra, and Rayalaseema. Combination of all the dialects is the language TELUGU and it is a perfect blend of elegance in Sanskrit, sweetness in Tamil along with the essence of Kannada language. The formation of dialects can be of different reasons. For speech processing research, till today there is no standard speech database created for Telugu dialects. In this paper we developed a speech database that can be utilized for the recognition of Telugu dialects and we had applied two modeling techniques that are, Hidden Markov Model (HMM) and Gaussian mixture model (GMM) in order to recognize the dialects of Telugu language by using speech independant utterances. We imposed Mel-Frequency Cepstral Coefficient for extracting the spectral features from the obtained speech data and observed that GMM provides better accurate results than HMM.

Mobility Aware Network Selection in a Heterogeneous Wireless Environment

Abstract Different Radio Access Technologies (RATs) coexist in the same area has encouraged the researchers to get profit from the available networks by selecting of the best RAT at each moment of the call session to satisfy the user requirements. In this paper, we address a real-world problem which is the frequent mobility of the users in heterogeneous networks. We present in this paper a framework that allows users to select the best networks for the whole call session especially form a mobility perspective. The framework consists of several steps, starting by the path prediction which is performed using a Markov model order 2. The second step is to make the network selection on the zones of each predicted path, while in the third step; we get the best RAT’s configuration for each predicted path. Finally, we use another function to select one of the best configurations to be used for all the possible used paths. The results show that our proposal performs very well by eliminating the unnecessary vertical handover while maintaining a good Quality of Service (QoS).

MODER2: first-order Markov modeling and discovery of monomeric and dimeric binding motifs.

MotivationPosition-specific probability matrices (PPMs, also called position-specific weight matrices) have been the dominating model for transcription factor (TF)-binding motifs in DNA. There is, however, increasing recent evidence of better performance of higher order models such as Markov models of order one, also called adjacent dinucleotide matrices (ADMs). ADMs can model dependencies between adjacent nucleotides, unlike PPMs. A modeling technique and software tool that would estimate such models simultaneously both for monomers and their dimers have been missing.ResultsWe present an ADM-based mixture model for monomeric and dimeric TF-binding motifs and an expectation maximization algorithm MODER2 for learning such models from training data and seeds. The model is a mixture that includes monomers and dimers, built from the monomers, with a description of the dimeric structure (spacing, orientation). The technique is modular, meaning that the co-operative effect of dimerization is made explicit by evaluating the difference between expected and observed models. The model is validated using HT-SELEX and generated datasets, and by comparing to some earlier PPM and ADM techniques. The ADM models explain data slightly better than PPM models for 314 tested TFs (or their DNA-binding domains) from four families (bHLH, bZIP, ETS and Homeodomain), the ADM mixture models by MODER2 being the best on average.Availability and implementationSoftware implementation is available from https://github.com/jttoivon/moder2.Supplementary information Supplementary data are available at Bioinformatics online.

Classification, positioning, and tracking of drones by HMM using acoustic circular microphone array beamforming

This paper addresses issues with monitoring systems that identify and track illegal drones. The development of drone technologies promotes the widespread commercial application of drones. However, the ability of a drone to carry explosives and other destructive materials may pose serious threats to public safety. In order to reduce these threats, we propose an acoustic-based scheme for positioning and tracking of illegal drones. Our proposed scheme has three main focal points. First, we scan the sky with switched beamforming to find sound sources and record the sounds using a microphone array; second, we perform classification with a hidden Markov model (HMM) in order to know whether the sound is a drone or something else. Finally, if the sound source is a drone, we use its recorded sound as a reference signal for tracking based on adaptive beamforming. Simulations are conducted under both ideal conditions (without background noise and interference sounds) and non-ideal conditions (with background noise and interference sounds), and we evaluate the performance when tracking illegal drones.

On the Steady State Performance of the Kalman Filter Applied to Acoustical Systems

The identification of transversal filters is important for numerous applications. In acoustical applications a first order Markov model is often used to describe the time-variant nature of transversal filters. The Kalman filter is the optimal unbiased estimator for such a Markov model. It inherently calculates the uncertainty of the current state estimate by its state error covariance matrix. In contrast to the broadband Kalman filter the covariance matrix of the exact Kalman filter depends on properties of the input signal. The single step covariance update of the exact Kalman filter is a discrete-time algebraic Riccati equation. We propose a solution for the steady state covariance matrix, which depends on the process parameters of the Markov model as well as properties of the input signal. It is derived based on the eigendecomposition of the covariance matrix and the autocorrelation matrix of the input signal. The proposed algorithm converges in few iterations and gives accurate results. We show how this result can be used to predict the steady state performance of the Kalman filter for system identification through numerical examples.

A Probabilistic Approach to Measuring Driving Behavior Similarity With Driving Primitives

Evaluating the similarity levels of driving behavior plays a pivotal role in driving style classification and analysis, thus benefiting the design of human-centric driver assistance systems. This article presents a novel framework capable of quantitatively measuring the similarity of driving behaviors for human based on driving primitives, i.e., the building blocks of driving behavior. To this end, we develop a Bayesian nonparametric method by integrating hierarchical Dirichlet process (HDP) with a hidden Markov model (HMM) in order to automatically extract the driving primitives from sequential observations without using any prior knowledge. Then, we propose a grid-based relative entropy approach, which allows quantifying the probabilistic similarity levels among these extracted primitives. Finally, the naturalistic driving data from 10 drivers are collected to evaluate the proposed framework, with comparison to traditional work. Experimental results demonstrate that the proposed probabilistic framework based on driving primitives can provide a quantitative measurement of similar levels of driving behavior associated with the dynamic and stochastic characteristics.

Multivariate Time Series Clustering and its Application in Industrial Systems

Multivariate Time Series (MTS) data obtained from large scale systems carry resourceful information about the internal system status. Multivariate Time Series Clustering is one of the exploratory methods that can enable one to discover the different types of behavior that is manifested in different working periods of a system. This knowledge can then be used for tasks such as anomaly detection or system maintenance. In this study, we make use of the statistical method, Variable Order Markov Models (VOMMs) to model each individual MTS and employ a new metric to calculate the distances between those VOMMs. The pairwise distances are then used to accomplish the MTS Clustering task. Two other MTS Clustering methods are presented and the superiority of the proposed method is confirmed with the experiments on two data sets from Cyber-Physical Systems. The computational complexity of the presented methods is also discussed.

Natural Language Statistical Features of LSTM-Generated Texts.

Long short-term memory (LSTM) networks have recently shown remarkable performance in several tasks that are dealing with natural language generation, such as image captioning or poetry composition. Yet, only few works have analyzed text generated by LSTMs in order to quantitatively evaluate to which extent such artificial texts resemble those generated by humans. We compared the statistical structure of LSTM-generated language to that of written natural language, and to those produced by Markov models of various orders. In particular, we characterized the statistical structure of language by assessing word-frequency statistics, long-range correlations, and entropy measures. Our main finding is that while both LSTM- and Markov-generated texts can exhibit features similar to real ones in their word-frequency statistics and entropy measures, LSTM-texts are shown to reproduce long-range correlations at scales comparable to those found in natural language. Moreover, for LSTM networks, a temperature-like parameter controlling the generation process shows an optimal value-for which the produced texts are closest to real language-consistent across different statistical features investigated.

Online human activity recognition employing hierarchical hidden Markov models

In the last few years there has been a growing interest in Human activity recognition (HAR) topic. Sensor-based HAR approaches, in particular, has been gaining more popularity owing to their privacy preserving nature. Furthermore, due to the widespread accessibility of the internet, a broad range of streaming-based applications such as online HAR, has emerged over the past decades. However, proposing sufficiently robust online activity recognition approach in smart environment setting is still considered as a remarkable challenge. This paper presents a novel online application of Hierarchical Hidden Markov Model in order to detect the current activity on the live streaming of sensor events. Our method consists of two phases. In the first phase, data stream is segmented based on the beginning and ending of the activity patterns. Also, on-going activity is reported with every receiving observation. This phase is implemented using Hierarchical Hidden Markov models. The second phase is devoted to the correction of the provided label for the segmented data stream based on statistical features. The proposed model can also discover the activities that happen during another activity - so-called interrupted activities. After detecting the activity pane, the predicted label will be corrected utilizing statistical features such as time of day at which the activity happened and the duration of the activity. We validated our proposed method by testing it against two different smart home datasets and demonstrated its effectiveness, which is competing with the state-of-the-art methods.

Using Hidden Markov Models to Uncover Underlying States in Neuroimaging Data for a Design Ideation Task

AbstractRecently, design researchers have begun to use neuroimaging methods (e.g., functional magnetic resonance imaging, fMRI) to understand a variety of cognitive processes relevant to design. However, common neuroimaging analysis techniques require significant assumptions relating temporal and spatial information during model formulation. In this work, we apply hidden Markov Models (HMM) in order to uncover patterns of brain activation in a design-relevant fMRI dataset. The underlying fMRI data comes from a prior research study in which participants generated solutions for twelve open-ended design problems from the literature. HMMs are generative models that are able to automatically infer the internal state characteristics of a process by observing state emissions. In this work, we demonstrate that distinct states can be extracted from the design ideation fMRI dataset, and that designers are likely to transition between a few key states. Additionally, the likelihood of occupancy within these states is different for high and low performing designers. This work opens up the door for future research to investigate the patterns of neural activation within the discovered states.

The dynamics of collective social behavior in a crowd controlled game

Despite many efforts, the behavior of a crowd is not fully understood. The advent of modern communication means has made it an even more challenging problem, as crowd dynamics could be driven by both human-to-human and human-technology interactions. Here, we study the dynamics of a crowd controlled game (Twitch Plays Pokémon), in which nearly a million players participated during more than two weeks. Unlike other online games, in this event all the players controlled exactly the same character and thus it represents an exceptional example of a collective mind working to achieve a certain goal. We dissect the temporal evolution of the system dynamics along the two distinct phases that characterized the game. We find that having a fraction of players who do not follow the crowd’s average behavior is key to succeed in the game. The latter finding can be well explained by an nth order Markov model that reproduces the observed behavior. Secondly, we analyze a phase of the game in which players were able to decide between two different modes of playing, mimicking a voting system. We show that the introduction of this system clearly polarized the community, splitting it in two. Finally, we discuss one of the peculiarities of these groups in the light of the social identity theory, which appears to describe well some of the observed dynamics.

On the existence of proper stochastic Markov models for statistical reconstruction and prediction of chaotic time series

Abstract In this paper, the problem of statistical reconstruction and prediction of chaotic systems with unknown governing equations using stochastic Markov models is investigated. Using the time series of only one measurable state, an algorithm is proposed to design any orders of Markov models and the approach is state transition matrix extraction. Using this modeling, two goals are followed: first, using the time series, statistical reconstruction is performed through which the probability density and conditional probability density functions are reconstructed; and second, prediction is performed. For this problem, some estimators are required and here the maximum likelihood and the conditional expected value are used. The efficiency of this algorithm has been investigated by applying it to some typical data. To illustrate the advantages of this model, a deterministic model based on the nearest neighbors in the delayed phase space is used. It will be seen that using the appropriate Markov model order and sufficient number of meshes, the proposed algorithm, unlike the deterministic model, is capable of reconstructing and predicting the chaotic data in a many steps.

Evaluating the performance of sequence encoding schemes and machine learning methods for splice sites recognition

Identification of splice sites is imperative for prediction of gene structure. Machine learning-based approaches (MLAs) have been reported to be more successful than the rule-based methods for identification of splice sites. However, the strings of alphabets should be transformed into numeric features through sequence encoding before using them as input in MLAs. In this study, we evaluated the performances of 8 different sequence encoding schemes i.e., Bayes kernel, density and sparse (DS), distribution of tri-nucleotide and 1st order Markov model (DM), frequency difference distance measure (FDDM), paired-nucleotide frequency difference between true and false sites (FDTF), 1st order Markov model (MM1), combination of both 1st and 2nd order Markov model (MM1 + MM2) and 2nd order Markov model (MM2) in respect of predicting donor and acceptor splice sites using 5 supervised learning methods (ANN, Bagging, Boosting, RF and SVM). The encoding schemes and machine learning methods were first evaluated in 4 species i.e., A. thaliana, C. elegans, D. melanogaster and H. sapiens, and then performances were validated with another four species i.e., Ciona intestinalis, Dictyostelium discoideum, Phaeodactylum tricornutum and Trypanosoma brucei. In terms of ROC (receiver-operating-characteristics) and PR (precision-recall) curves, FDTF encoding approach achieved higher accuracy followed by either MM2 or FDDM. Further, SVM was found to achieve higher accuracy (in terms of ROC and PR curves) followed by RF across encoding schemes and species. In terms of prediction accuracy across species, the SVM-FDTF combination was optimum than other combinations of classifiers and encoding schemes. Further, splice site prediction accuracies were observed higher for the species with low intron density. To our limited knowledge, this is the first attempt as far as comprehensive evaluation of sequence encoding schemes for prediction of splice sites is concerned. We have also developed an R-package EncDNA (https://cran.r-project.org/web/packages/EncDNA/index.html) for encoding of splice site motifs with different encoding schemes, which is expected to supplement the existing nucleotide sequence encoding approaches. This study is believed to be useful for the computational biologists for predicting different functional elements on the genomic DNA.

Fusing gene expressions and transitive protein-protein interactions for inference of gene regulatory networks

BackgroundSystematic fusion of multiple data sources for Gene Regulatory Networks (GRN) inference remains a key challenge in systems biology. We incorporate information from protein-protein interaction networks (PPIN) into the process of GRN inference from gene expression (GE) data. However, existing PPIN remain sparse and transitive protein interactions can help predict missing protein interactions. We therefore propose a systematic probabilistic framework on fusing GE data and transitive protein interaction data to coherently build GRN.ResultsWe use a Gaussian Mixture Model (GMM) to soft-cluster GE data, allowing overlapping cluster memberships. Next, a heuristic method is proposed to extend sparse PPIN by incorporating transitive linkages. We then propose a novel way to score extended protein interactions by combining topological properties of PPIN and correlations of GE. Following this, GE data and extended PPIN are fused using a Gaussian Hidden Markov Model (GHMM) in order to identify gene regulatory pathways and refine interaction scores that are then used to constrain the GRN structure. We employ a Bayesian Gaussian Mixture (BGM) model to refine the GRN derived from GE data by using the structural priors derived from GHMM. Experiments on real yeast regulatory networks demonstrate both the feasibility of the extended PPIN in predicting transitive protein interactions and its effectiveness on improving the coverage and accuracy the proposed method of fusing PPIN and GE to build GRN.ConclusionThe GE and PPIN fusion model outperforms both the state-of-the-art single data source models (CLR, GENIE3, TIGRESS) as well as existing fusion models under various constraints.

Next Location Prediction Based on an Adaboost-Markov Model of Mobile Users.

As an emerging class of spatial trajectory data, mobile user trajectory data can be used to analyze individual or group behavioral characteristics, hobbies and interests. Besides, the information extracted from original trajectory data is widely used in smart cities, transportation planning, and anti-terrorism maintenance. In order to identify the important locations of the target user from his trajectory data, a novel division method for preprocessing trajectory data is proposed, the feature points of original trajectory are extracted according to the change of trajectory structural, and then important locations are extracted by clustering the feature points, using an improved density peak clustering algorithm. Finally, in order to predict next location of mobile users, a multi-order fusion Markov model based on the Adaboost algorithm is proposed, the model order k is adaptively determined, and the weight coefficients of the 1~k-order models are given by the Adaboost algorithm according to the importance of various order models, a multi-order fusion Markov model is generated to predict next important location of the user. The experimental results on the real user trajectory dataset Geo-life show that the prediction performance of Adaboost-Markov model is better than the multi-order fusion Markov model with equal coefficient, and the universality and prediction performance of Adaboost-Markov model is better than the first to third order Markov models.

Analysis of Accounts Receivable Aging Using Variable Order Markov Model

An accurate prediction on near-future cash flows plays an important role for a company to attenuate the shortage risk of cash flow by preparing a plan for future investment in advance. Unfortunately, there exists a high level of uncertainty in the types of transactions that occur in the form of receivables in inter-company transactions, unlike other types of transactions, thereby making the prediction of cash flows difficult. In this study, we analyze the trend of cash flow related to account receivables that may arise between firms, by using a stochastic approach. In particular, we utilize Variable Order Markov (VOM) model to predict how future cash flows will change based on cash flow history. As a result of this study, we show that the average accuracy of the VOM model increases about 12.5% or more compared with that of other existing techniques.

Asymptotically Tight Bounds on the Depth of Estimated Context Trees

We study the maximum Markov model order that can be estimated for an (individual) input sequence $x$ of length $n$ over a finite alphabet of size $\alpha $ , for popular estimators of tree models, where the estimated order is determined by the depth of a context tree estimate, and in the special case of plain Markov models, where the tree is constrained to be perfect (with all leaves at the same depth). First, we consider penalized maximum likelihood estimators where a context tree $\hat {T}$ is obtained by minimizing a cost of the form $-\log \hat {P}_{T}(x) + f(n)|S_{T}|$ , where $\hat {P}_{T}(x)$ is the ML of $x$ under a model with context tree $T$ , $S_{T}$ is the set of leaves of $T$ , and $f(n)$ is an increasing (penalization) function of $n$ (the popular BIC estimator is a special case with $f(n)=\frac {\alpha -1}{2}\log n$ ). For plain Markov models, a simple argument yields a known upper bound $\overline {k}(n) = O(\log n)$ on the maximum order that can be estimated for $x$ , and we show that, in fact, this simple bound is not far from tight. For general context trees, we derive an asymptotic upper bound, $n^{1/2 - o(1)}$ , on the estimated depth, and we exhibit explicit input sequences that asymptotically attain the bound up to a multiplicative constant factor. We show that a similar upper bound applies also to MDL estimators based on the KT probability assignment and, moreover, the same example sequences asymptotically approach the upper bound also in this case.

Cost effectiveness and affordability of trastuzumab in sub-Saharan Africa for early stage HER2-positive breast cancer

BackgroundBreast cancer is the second most common cancer worldwide, the most common among women, and the most frequent cause of death among women in less developed regions. Trastuzumab is a humanized monoclonal antibody that downregulates the extracellular domain of the HER2 protein. Using trastuzumab to treat women with localized HER2-positive breast cancer has been shown to improve survival. The objective of this study is to explore the cost-effectiveness of adjuvant trastuzumab, from a societal perspective, in 11 African countries. In addition, we aimed to establish value-based prices for trastuzumab based on the gross domestic product per capita in each country.MethodsWe developed a Markov model in order to assess the costs and benefits associated with trastuzumab treatment over a lifetime horizon. A probabilistic sensitivity analysis was performed in order to estimate the impact of uncertainty of parameter-values on the results. Efficacy inputs were derived using clinical trial data from non-African countries.ResultsIn the base case analysis, trastuzumab yielded a gain ranging from 0.92 LYs in Nigeria to 1.07 LYs in South Africa, and 0.9 QALYs in Nigeria to 1.02 QALYs in South Africa. The incremental cost ranged from 19,561 USD in Nigeria to 19,997 USD in Congo, and an incremental cost-effectiveness ratio ranging from 19,534 USD/QALY in South Africa to 21,697 USD/QALY in Nigeria. Using willingness to pay estimates based on World Health Organization recommendations, trastuzumab appear to not be cost-effective in all countries analyzed. Cost-effectiveness estimates were most sensitive to the discount rate, trastuzumab cost, and the hazard ratio.ConclusionsTrastuzumab does not appear to be cost effective in the African countries analyzed. In order for trastuzumab to be cost-effective, the costs of treatment would require significant discounts.