Order Markov Model Research Articles

A novel method for splice sites prediction using sequence component and hidden Markov model.

With increasing growth of DNA sequence data, it has become an urgent demand to develop new methods to accurately predict the genes. The performance of gene detection methods mainly depend on the efficiency of splice site prediction methods. In this paper, a novel method for detecting splice sites is proposed by using a new effective DNA encoding method and AdaBoost.M1 classifier. Our proposed DNA encoding method is based on multi-scale component (MSC) and first order Markov model (MM1). It has been applied to the HS3D dataset with repeated 10 fold cross validation. The experimental results indicate that the new method has increased the classification accuracy and outperformed some current methods such as MM1-SVM, Reduced MM1-SVM, SVM-B, LVMM, DM-SVM, DM2-AdaBoost and MS C+Pos(+APR)-SVM.

An Estimation of Thailand's Hourly Solar Radiation Using Markov Transition Matrix Method

To estimate global solar radiation from easy available weather forecast data (sky condition), Markov model is used for this estimation. The five-year (1996-2000) global radiation data that are taken at an hour intervals from Nakhon Pathom station, Thailand (latitude 13.81ºN and longitude 100.04ºE) are used to construct the Markov transition matrices. The global radiation sequences in 2000 will be generated by based on the characteristic probability of moving global radiation values which were observed from the obtained data during 1996-1999. The autocorrelation function is used for checking the order of probability of moving obtained data. In this study, the five first and five second-order Markov transition matrices (MTMs), which are selected from the autocorrelation functions, are constructed, each MTMs will be used for generating global radiation values in each day with different sky conditions (clear, partly cloudy, mostly cloudy, cloudy and overcast). From the results of comparison between the statistical characteristics of observed and two synthetic generated data, global radiation data behavior slightly improved by the second order Markov model.

Dynamic Behavior Selection Model based on Emotional States for Conbe-I robot

Currently, the rapid development of non-industrial robots that are designed with artificial intelligence (AI) methods to improve the robotics system is to have them imitate human thinking and behavior. Therefore, our works have focused on studying and investigating the application of brain-inspired technology for developing the conscious behavior robot (Conbe-I). We created the hierarchical structure model, which is called “Consciousness-Based Architecture: CBA” module, but it has limitation in managing and selecting the behavior that only depends on the increase and decrease of the motivation levels. Consequently, in this paper, we would like to introduce the dynamic behavior selection model based on emotional states, which develops by Self-organizing map learning and Markov model in order to define the relationship between the behavioral selection and emotional expression model. We confirm the effectiveness of the proposed system with the experimental results.

Predicting the Affordable Rate in Interference-Limited Cellular Systems Using Higher-Order Markov Models

In cellular broadband access systems, such as 3GPP long-term evolution, the user equipment (UE) feeds back a quantized rate metric to the base station (eNodeB), in order to perform rate adaptation. The periodicity of this rate feedback is fixed so as to minimize the overhead without eroding its benefits. However, between two feedback instants $n$ and $n+\delta $ , the actual rate that the UE can correctly decode might change due to the: 1) Doppler shift and 2) change in the active set of interferers. Hence, to fully exploit the benefits of adaptation, an accurate prediction of the attainable rate is required. In this context, we argue that a non-parametric approach to rate prediction is necessary. Since the selected rate is from a set of discrete values, the rate prediction problem is mapped by us onto a discrete sequence prediction problem, and we construct higher order Markov models for the discrete sequences using source encoding algorithms. We then propose two distinct rate-prediction algorithms. One of them is the adaptive maximum $a~posteriori$ estimator, while the other is the adaptive Bayesian risk-based estimator. Both of these algorithms simultaneously estimate the best Markov model for each UE and then perform prediction based on the estimated model.

Whole genome/proteome based phylogeny reconstruction for prokaryotes using higher order Markov model and chaos game representation

Traditional methods for sequence comparison and phylogeny reconstruction rely on pair wise and multiple sequence alignments. But alignment could not be directly applied to whole genome/proteome comparison and phylogenomic studies due to their high computational complexity. Hence alignment-free methods became popular in recent years. Here we propose a fast alignment-free method for whole genome/proteome comparison and phylogeny reconstruction using higher order Markov model and chaos game representation. In the present method, we use the transition matrices of higher order Markov models to characterize amino acid or DNA sequences for their comparison. The order of the Markov model is uniquely identified by maximizing the average Shannon entropy of conditional probability distributions. Using one-dimensional chaos game representation and linked list, this method can reduce large memory and time consumption which is due to the large-scale conditional probability distributions. To illustrate the effectiveness of our method, we employ it for fast phylogeny reconstruction based on genome/proteome sequences of two species data sets used in previous published papers. Our results demonstrate that the present method is useful and efficient.Availability and implementation: The source codes for our algorithm to get the distance matrix and genome/proteome sequences can be downloaded from ftp://121.199.20.25/. The software Phylip and EvolView we used to construct phylogenetic trees can be referred from their websites.

Learning hierarchical spatio-temporal pattern for human activity prediction

Human activity prediction has become increasingly valuable in many applications. This paper, initially from the perspective of cognition science, presents a novel approach to learning a hierarchical spatio-temporal pattern of human activities to predict ongoing activities from videos that contain only the onsets of the activities. Spatio-temporal pattern can be learned by a Hierarchical Self-Organizing Map (HSOM), which consists of two self-organizing maps (i.e., action map and actionlet map) connected via associative links trained by Hebbian learning. Ongoing activities can be predicted by Variable order Markov Model (VMM), which provides the means for capturing both large and small order Markov dependencies based on the training actionlet sequences. Experiments of the proposed method on four challenging 3D action datasets captured by commodity depth cameras show promising results.

A Novel Variable Order Markov Model Based Wireless Service Prediction Algorithm with User Similarity

A Novel Variable Order Markov Model Based Wireless Service Prediction Algorithm with User Similarity

Designing virtual bots for optimizing strategy-game groups

In the last decade, a permanent increasing in the popularity of videogames has happened. As the cost of the computational power has decreased, graphic games more realistic have been developed. However, although everybody in this industry knew that an intelligent and believable behavior of bots (or, in general, non-playing characters) is an important key to make a game fun to play, the experts have not showed real interests in this issue until recently. In this paper, we propose how a good strategy for optimizing the behavior of a team of bots (with roles between members and communication skills between each other) in the “capture the flag game” domain, could be designed and analyzed using a combination between swarming optimization techniques and mathematical analysis based in Markov models in order to improve the standard strategies that videogames use. This domain presents a particular case of the “exploration vs. exploitation” dilemma, a paradox that appears in numerous situations where systems needs being adaptable and learnable at the same time and solutions of the dilemma are built evolving balances between parts. The environment used to test the proposed model will be the Unreal Tournament virtual world.

Generating structured music for bagana using quality metrics based on Markov models

In this research, a system is built that generates bagana music, a traditional lyre from Ethiopia, based on a first order Markov model. Due to the size of many datasets it is often only possible to get rich and reliable statistics for low order models, yet these do not handle structure very well and their output is often very repetitive. A first contribution of this paper is to propose a method that allows the enforcement of structure and repetition within music, thus handling long term coherence with a first order model. The second goal of this research is to explain and propose different ways in which low order Markov models can be used to build quality assessment metrics for an optimization algorithm. These are then implemented in a variable neighborhood search algorithm that generates bagana music. The results are examined and thoroughly evaluated.

Research on DNA Sequence Homology Based on Second Order Markov Model

Research on DNA Sequence Homology Based on Second Order Markov Model

A web recommendation system considering sequential information

With the rapid growth of information technology, the current era is witnessing an exponential increase in the generation and collection of web data. Projecting the right information to the right person is becoming more difficult day by day, which in turn adds complexity to the decision making process. Recommendation systems are intelligent systems that address this issue. They are widely used in e-commerce websites to recommend products to users. Most of the popular recommendation systems consider only the content information of users and ignore sequential information. Sequential information also provides useful insights about the behavior of users. We have developed a novel system that considers sequential information present in web navigation patterns, along with content information. We also consider soft clusters during clustering, which helps in capturing the multiple interests of users. The proposed system has utilized similarity upper approximation and singular value decomposition (SVD) for the generation of recommendations for users. We tested our approach on three datasets, the MSNBC benchmark dataset, simulated dataset and CTI dataset. We compared our approach with the first order Markov model as well as random prediction model. The results validate the viability of our approach.

Learning and prediction of relational time series

Learning to predict events in the near future is fundamental to human and artificial agents. Many prediction techniques are unable to learn and predict a stream of relational data online when the environments are unknown, non-stationary, and no prior training examples are available. This paper addresses the online prediction problem by introducing a low complexity learning technique called Situation Learning and several prediction techniques that use the information from Situation Learning to predict the next likely event. The prediction techniques include two variants of a Bayesian inference technique, a variable order Markov model prediction technique and situation matching techniques. We compared their prediction accuracies quantitatively for three domains: a role-playing game, computer network intrusion system alerts, and event prediction of maritime paths in a discrete-event simulator.

An Improved Method for Splice Site Prediction in DNA Sequences Using Support Vector Machines

A vast amount of sequence data has been generated due to advancements in DNA sequencing technology. This exponential increase requires new and efficient methods for the analysis of DNA sequence data. Predicting genes in this newly sequenced data is an important and essential step towards genome annotation. Genome annotation helps in determining function of these genes. Accurate splice site prediction in DNA sequences leads to correct gene structure prediction in eukaryotes and it requires effective modelling of regions surrounding these sites. A large number of methods for splice site prediction are available in literature but very few of them are suitable to be incorporated as gene prediction module because of their complexity. In this paper, a splice site prediction method based on second order markov model and support vector machine is developed. This method shows improvement over most of the existing splice site predictors in use today. The experimental results suggest that second order markov model is an effective pre-processing approach. This approach when combined with support vector machine provides better classification accuracy in predicting splice sites.

Optimizing Sparse Mean Reverting Portfolios with AR-HMMs in the Presence of Secondary Effects

In this paper we optimize mean reverting portfolios subject to cardinality constraints. First, the parameters of the corresponding Ornstein-Uhlenbeck (OU) process are estimated by auto-regressive Hidden Markov Models (AR-HMM) in order to capture the underlying characteristics of the financial time series. Portfolio optimization is then performed according to maximizing the mean return by the means of the introduced AR-HMM prediction algorithm. The optimization itself is carried out by stochastic search algorithms. The presented solutions satisfy the cardinality constraint thus providing a sparse portfolios which minimizes the transaction costs and maximizes the interpretability of the results. The performance has been tested on historical data obtained from S&P 500 and FOREX. The results demonstrate that a good average return can be achieved by the proposed AR-HMM based trading algorithms in realistic scenarios. Furthermore, profitability can also be accomplished in the presence of secondary effects.

Analysis of pattern overlaps and exact computation of P-values of pattern occurrences numbers: case of Hidden Markov Models.

BackgroundFinding new functional fragments in biological sequences is a challenging problem. Methods addressing this problem commonly search for clusters of pattern occurrences that are statistically significant. A measure of statistical significance is the P-value of a number of pattern occurrences, i.e. the probability to find at least S occurrences of words from a pattern in a random text of length N generated according to a given probability model. All words of the pattern are supposed to be of same length.ResultsWe present a novel algorithm SufPref that computes an exact P-value for Hidden Markov models (HMM). The algorithm is based on recursive equations on text sets related to pattern occurrences; the equations can be used for any probability model. The algorithm inductively traverses a specific data structure, an overlap graph. The nodes of the graph are associated with the overlaps of words from . The edges are associated to the prefix and suffix relations between overlaps. An originality of our data structure is that pattern need not be explicitly represented in nodes or leaves. The algorithm relies on the Cartesian product of the overlap graph and the graph of HMM states; this approach is analogous to the automaton approach from JBCB 4: 553-569. The gain in size of SufPref data structure leads to significant improvements in space and time complexity compared to existent algorithms. The algorithm SufPref was implemented as a C++ program; the program can be used both as Web-server and a stand alone program for Linux and Windows. The program interface admits special formats to describe probability models of various types (HMM, Bernoulli, Markov); a pattern can be described with a list of words, a PSSM, a degenerate pattern or a word and a number of mismatches. It is available at http://server2.lpm.org.ru/bio/online/sf/. The program was applied to compare sensitivity and specificity of methods for TFBS prediction based on P-values computed for Bernoulli models, Markov models of orders one and two and HMMs. The experiments show that the methods have approximately the same qualities.Electronic supplementary materialThe online version of this article (doi:10.1186/s13015-014-0025-1) contains supplementary material, which is available to authorized users.

Modeling anger and aggressive driving behavior in a dynamic choice–latent variable model

This paper develops a hybrid choice–latent variable model combined with a Hidden Markov model in order to analyze the causes of aggressive driving and forecast its manifestations accordingly. The model is grounded in the state–trait anger theory; it treats trait driving anger as a latent variable that is expressed as a function of individual characteristics, or as an agent effect, and state anger as a dynamic latent variable that evolves over time and affects driving behavior, and that is expressed as a function of trait anger, frustrating events, and contextual variables (e.g., geometric roadway features, flow conditions, etc.). This model may be used in order to test measures aimed at reducing aggressive driving behavior and improving road safety, and can be incorporated into micro-simulation packages to represent aggressive driving. The paper also presents an application of this model to data obtained from a driving simulator experiment performed at the American University of Beirut. The results derived from this application indicate that state anger at a specific time period is significantly affected by the occurrence of frustrating events, trait anger, and the anger experienced at the previous time period. The proposed model exhibited a better goodness of fit compared to a similar simple joint model where driving behavior and decisions are expressed as a function of the experienced events explicitly and not the dynamic latent variable.

A statistical approach for 5' splice site prediction using short sequence motifs and without encoding sequence data.

BackgroundMost of the approaches for splice site prediction are based on machine learning techniques. Though, these approaches provide high prediction accuracy, the window lengths used are longer in size. Hence, these approaches may not be suitable to predict the novel splice variants using the short sequence reads generated from next generation sequencing technologies. Further, machine learning techniques require numerically encoded data and produce different accuracy with different encoding procedures. Therefore, splice site prediction with short sequence motifs and without encoding sequence data became a motivation for the present study.ResultsAn approach for finding association among nucleotide bases in the splice site motifs is developed and used further to determine the appropriate window size. Besides, an approach for prediction of donor splice sites using sum of absolute error criterion has also been proposed. The proposed approach has been compared with commonly used approaches i.e., Maximum Entropy Modeling (MEM), Maximal Dependency Decomposition (MDD), Weighted Matrix Method (WMM) and Markov Model of first order (MM1) and was found to perform equally with MEM and MDD and better than WMM and MM1 in terms of prediction accuracy.ConclusionsThe proposed prediction approach can be used in the prediction of donor splice sites with higher accuracy using short sequence motifs and hence can be used as a complementary method to the existing approaches. Based on the proposed methodology, a web server was also developed for easy prediction of donor splice sites by users and is available at http://cabgrid.res.in:8080/sspred.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-014-0362-6) contains supplementary material, which is available to authorized users.

Accurate Assignment of Significance to Neuropeptide Identifications Using Monte Carlo K-Permuted Decoy Databases

In support of accurate neuropeptide identification in mass spectrometry experiments, novel Monte Carlo permutation testing was used to compute significance values. Testing was based on k-permuted decoy databases, where k denotes the number of permutations. These databases were integrated with a range of peptide identification indicators from three popular open-source database search software (OMSSA, Crux, and X! Tandem) to assess the statistical significance of neuropeptide spectra matches. Significance p-values were computed as the fraction of the sequences in the database with match indicator value better than or equal to the true target spectra. When applied to a test-bed of all known manually annotated mouse neuropeptides, permutation tests with k-permuted decoy databases identified up to 100% of the neuropeptides at p-value < 10−5. The permutation test p-values using hyperscore (X! Tandem), E-value (OMSSA) and Sp score (Crux) match indicators outperformed all other match indicators. The robust performance to detect peptides of the intuitive indicator “number of matched ions between the experimental and theoretical spectra” highlights the importance of considering this indicator when the p-value was borderline significant. Our findings suggest permutation decoy databases of size 1×105 are adequate to accurately detect neuropeptides and this can be exploited to increase the speed of the search. The straightforward Monte Carlo permutation testing (comparable to a zero order Markov model) can be easily combined with existing peptide identification software to enable accurate and effective neuropeptide detection. The source code is available at http://stagbeetle.animal.uiuc.edu/pepshop/MSMSpermutationtesting.

A hybrid model for business process event and outcome prediction

AbstractLarge service companies run complex customer service processes to provide communication services to their customers. The flawless execution of these processes is essential because customer service is an important differentiator. They must also be able to predict if processes will complete successfully or run into exceptions in order to intervene at the right time, preempt problems and maintain customer service. Business process data are sequential in nature and can be very diverse. Thus, there is a need for an efficient sequential forecasting methodology that can cope with this diversity. This paper proposes two approaches, a sequential k nearest neighbour and an extension of Markov models both with an added component based on sequence alignment. The proposed approaches exploit temporal categorical features of the data to predict the process next steps using higher order Markov models and the process outcomes using sequence alignment technique. The diversity aspect of the data is also added by considering subsets of similar process sequences based on k nearest neighbours. We have shown, via a set of experiments, that our sequential k nearest neighbour offers better results when compared with the original ones; our extension Markov model outperforms random guess, Markov models and hidden Markov models.

Changes in note order stereotypy during learning in two species of songbird, measured with automatic note classification

In addition to mastering the task of performing the individual notes of a song, many songbirds must also learn to produce each note in a stereotyped order. As a bird practices its new song, it may perform thousands of bouts, providing a rich source of information about how note phonology and note type order change during learning. A combination of acoustic landmark descriptions, neural network and hierarchical clustering classifiers, and Markov models of note order made it possible to measure note order stereotypy in two species of songbird. Captive swamp sparrows (Melospiza melodia, 11 birds, 92063 notes/bird), and wild tree swallows (Tachycineta bicolor, 18 birds, 448 syllables/bird) were recorded song development. The predictability of swamp sparrow note order showed significant increase during the month-long recording period (F1,162 = 9977, p &amp;lt; 0.001). Note order stereotypy in tree swallows also increased by a significant amount over a month-long field season (Mann-Whitney V = 12, p-value &amp;lt; 0.001). Understanding changes in song stereotypy can improve our knowledge of vocal learning, performance, and cultural transmission.