Increasing the accuracy of single-molecule data analysis using tMAVEN

This paper proposes a novel similarity measure between vector sequences. We work in the framework of model-based approaches, where each sequence is first mapped to a Hidden Markov Model (HMM) and then a measure of similarity is computed between the HMMs. We propose to model sequences with semicontinuous HMMs (SC-HMMs). This is a particular type of HMM whose emission probabilities in each state are mixtures of shared Gaussians. This crucial constraint provides two major benefits. First, the a priori information contained in the common set of Gaussians leads to a more accurate estimate of the HMM parameters. Second, the computation of a similarity between two SC-HMMs can be simplified to a Dynamic Time Warping (DTW) between their mixture weight vectors, which significantly reduces the computational cost. Experiments are carried out on a handwritten word retrieval task in three different datasets-an in-house dataset of real handwritten letters, the George Washington dataset, and the IFN/ENIT dataset of Arabic handwritten words. These experiments show that the proposed similarity outperforms the traditional DTW between the original sequences, and the model-based approach which uses ordinary continuous HMMs. We also show that this increase in accuracy can be traded against a significant reduction of the computational cost.

A new type of hidden Markov model (HMM) developed based on the fuzzy clustering result is proposed for identification of human motion. By associating the human continuous movements with a series of human motion primitives, the complex human motion could be analysed as the same process as recognizing a word by alphabet. However, because the human movements can be multi-paths and inherently stochastic, it is indisputable that a more sophisticated framework must be applied to reveal the statistic relationships among the different human motion primitives. Hence, based on the human motion recognition results derived from the fuzzy clustering function, HMM is modified by changing the formulation of the emission and transition matrices to analyse the human wrist motion. According to the experimental results, the complex human wrist motion sequence can be identified by the novel HMM holistically and efficiently

In this paper we propose a new framework for modeling 2D shapes. A shape is first described by a sequence of local features (e.g., curvature) of the shape boundary. The resulting description is then used to build a profile hidden Markov model (PHMM) representation of the shape. PHMMs are a particular type of hidden Markov models (HMMs) with special states and architecture that can tolerate considerable shape contour perturbations, including rigid and non-rigid deformations, occlusions and missing contour parts. Different from traditional HMM-based shape models, the sparseness of the PHMM structure allows efficient inference and learning algorithms for shape modeling and analysis. The new framework can be applied to a wide range of problems, from shape matching and classification to shape segmentation. Our experimental results show the effectiveness and robustness of this new approach in the three application domains.

This paper is concerned with filtering of hidden Markov processes (HMP) which possess (or approximately possess) the property of lumpability. This property is a generalization of the property of lumpability of a Markov chain which has been previously addressed by others. In essence, the property of lumpability means that there is a partition of the (atomic) states of the Markov chain into aggregated sets which act in a similar manner as far as the state dynamics and observation statistics are concerned. We prove necessary and sufficient conditions on the HMP for exact lumpability to hold. For a particular class of hidden Markov models (HMM), namely finite output alphabet models, conditions for lumpability of all HMP representable by a specified HMM are given. The corresponding optimal filter algorithms for the aggregated states are then derived. The paper also describes an approach to efficient suboptimal filtering for HMP which are approximately lumpable. By this we mean that the HMM generating the process may be approximated by a lumpable HMM. This approach involves directly finding a lumped HMM which approximates the original HMM well, in a matrix norm sense. An alternative approach for model reduction based on approximating a given HMM by an exactly lumpable HMM is also derived. This method is based on the alternating convex projections algorithm. Some simulation examples are presented which illustrate the performance of the suboptimal filtering algorithms.

Computational cancer phylogenetics can play an important role in delineating possible tumor progression pathways and identifying molecular subtypes and mechanisms of action. We previously developed a pipeline for constructing tumor phylogenies from recurring cell types computationally inferred from whole genome copy number data. The accuracy and detail of these tumor phylogenies, however, depends on the identification of accurate and high-resolution molecular markers of progression, i.e., reproducible regions of copy number variation that can be used to robustly differentiate different subtypes and stages of progression. Here we present a new method for the problem using hidden Markov models (HMMs) to derive robust, high resolution progression markers from sets of tumor samples. We demonstrate our method on a publicly available array comparative genome hybridization (aCGH) dataset (NCBI GEO GSE16672, Navin et al., 2010) from sectioned primary ductal breast tumors. Our method uses an HMM, a class of probabilistic models, to classify sets of aCGH data into a partitioning of samples into normal (diploid) or amplified at each copy number probe. It differs from other similar HMM methods primarily in seeking a parsimonious set of combinations of amplification states able to explain all aCGH profiles simultaneously in order to identify robust markers of progression across samples. The model learns frequencies with which different combinations of amplifications are observed across the samples by modeling individual probes as Gaussian random variables with either normal or tetraploid means, with data more consistent with tetraploid being classified as amplified and those more consistent with diploid classified as normal. To handle a combinatorial explosion in combinations of amplification states with increasing numbers of samples, the method introduces a Gibbs sampling algorithm to learn a parsimonious model of the most frequently occurring combinations of amplification states. We applied our methods to a previously constructed set of inferred cell types derived from the Navin et al. data (Tolliver et al., 2010) and to a comparison set of 9 random samples from the raw aCGH data. We validated our model relative to manual labeling of amplicons on the same data. In both experiments, the HMM method was able to pick up significantly larger numbers of robustly amplified segments per chromosome than did prior methods or manual analysis. The resulting segments can be directly fed into downstream analysis routines for phylogeny inference or other predictions. In future work, the HMM method may be improved by fine-tuning the underlying model for copy number variation. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 3964. doi:1538-7445.AM2012-3964

The discovery of unique substrates is important for developing potential applications of enzymes. However, the experimental procedures for substrate identification are laborious, time-consuming, and expensive. Although in silico structure-based approaches show great promise, recent extensive studies have shown that these approaches remain a formidable challenge for current biocomputational methodologies. Here we present an open-source, extensible, and flexible software platform for predicting enzyme substrates called THEMIS, which performs in silico virtual screening for potential catalytic targets of an enzyme on the basis of the enzyme's catalysis mechanism. On the basis of a generalized transition state theory of enzyme catalysis, we introduce a modified docking procedure called "mechanism-based restricted docking" (MBRD) for novel substrate recognition from molecular docking. Comprising a series of utilities written in C/Python, THEMIS automatically executes parallel-computing MBRD tasks and evaluates the results with various molecular mechanics (MM) criteria such as energy, distance, angle, and dihedral angle to help identify desired substrates. Exhaustive sampling and statistical measures were used to improve the robustness and reproducibility of the method. We used Candida antarctica lipase B (CALB) as a test system to demonstrate the effectiveness of our computational prediction of (non)substrates. A novel MM score function for CALB substrate identification derived from the near-attack conformation was used to evaluate the possibility of chemical transformation. A highly positive rate of 93.4% was achieved from a CALB substrate library with 61 known substrates and 35 nonsubstrates, and the screening rate has reached 103 compounds/day (96 CPU cores, 100 samples/compound). The performance shows that the present method is perhaps the first reported scheme to meet the requirement for practical applicability to enzyme studies. An additional study was performed to validate the universality of our method. In this verification we employed two distinct enzymes, nitrilase Nit6803 and SDR Gox2181, where the correct rates of both enzymes exceeded 90%. The source code used will be released under the GNU General Public License (GPLv3) and will be free to download. We believe that the present method will provide new insights into enzyme research and accelerate the development of novel enzyme applications.

Hidden Markov model (HMM) is widely used in time series modeling. Usually, it is necessarily to calculate the sequence’s likelihood w.r.t. HMM to evaluate the similarity between the sequence and the HMM. Hence, it is required to provide a method to select a best threshold value that can determine whether the sequence is well approximated by the model or not. However, this process is usually done manually. Here, we provide a method (HTDM) to determine the threshold automatically. Based on likelihood statistic, we conclude that the likelihood is subjected to normal distribution, and then standard deviation of the distribution is estimated. Hence, the distance threshold value can be achieved based on the rule of “three sigma”. In the experiment, we make performance comparison between the HMM-based hierarchical clustering algorithm HHCH using HTDM, and algorithm HBHCTS in which threshold is set by manual. Experiment results show that the proposed method is effective on both syntax dataset and real world dataset.

Our aim consists in developing a software which can recognize M trading patterns in real time using Hidden Markov Models (HMMs). A trading pattern is a predefined figure indicating a specific behavior of prices. We trained M + 1 HMMs using Baum-Welch Algorithm combined with Genetic Algorithm. In particular, with HMMs we describe M trading patterns while the other one, called threshold model, can recognize all the not predefined patterns. The classification algorithm correctly recognizes 93% of the provided patterns. Thanks to the analysis of the false positive examples, we finally designed some more filters to reduce them.

In this paper we develop and validate a procedure for testing against a shift in mean in the observa-tions and hidden state sequence of state space models with Gaussian noise. State space models are popular for modelling stochastic networks as they allow to take into account that observations of the true state of a sys-tem may be corrupted by measurement noise (usually, a Gaussian noise process is assumed). Although state space models are very general, they are still relatively tractable in that the true system state can be estimated efficiently by a recursive procedure known as Kalman filtering. State space models can be regarded as a special type of hidden Markov model. As such, they are a flexible modelling tool that has been found useful, for example, for modelling road networks (Stathopoulos and Karlaftis, 2003) to account for uncertainty in the measurement of travel times. For instance, we may assume that travel times have to be estimated from flow and occupancy data. An increase in the unobserved mean travel time can be caused by traffic congestion; a shift in the mean value of the observations on the other hand could indicate a bias of the sensors. State space models can also be used to model communication networks: Suppose that the current state of a channel (e.g. measured by the probability of packet loss) is not observed directly, but has to be inferred from the received package flow. A change in the mean value of the hidden state sequence, or a change in the mean value of the received package flow can deteriorate the performance of the network if it remains unrecognised. This motivates us to investigate procedures for testing against a shift in mean in the observations and hidden state sequence of state space models. The objective is to detect a change as quickly as possible while keeping the ratio of false alarms at a pre-specified low level. Since the observations are generally not independent, in (Basseville and Nikiforov, 1993) a cumulative sum (CUSUM) procedure is applied to the (independent) sequence of innovations, which is obtained as a byproduct from Kalman filter estimation of the hidden states. That is, a log-likelihood ratio (LLR) test statistic is used and an alarm is raised as soon as this test statistic exceeds a certain threshold that is assumed to be given. Change point detection for state space models has also been considered in (Lai and Shan, 1999) for the case where the size of the mean shift is unknown, in which case a generalized LLR test can be applied. In this paper we tackle the question of how the threshold of the sequential LLR test can be chosen when the shift size is assumed to be known. In practice, the latter assumption can be dealt with by realizing that typically there will be a minimum change size that is of interest from an engineering perspective, and that can thus be used as input for the model. Based on this assumption we can identify the appropriate level of the threshold based on approximations of the false alarm probability - essentially the probability that a random walk process exceeds a given threshold on an interval. A persistent change in the mean value of the observations results in a dynamic change in the mean value of the innovations, which are therefore not identically distributed after the change point. However, it follows from the stability properties of the Kalman filter that under weak conditions the magnitude of the shift converges to a constant. This allows for large-deviations (LD) approximations as well as approximations motivated by a functional central limit theorem (CLT). LD approximations to the false alarm probability have been considered in (Bucklew, 1985; Ellens et al., 2013; Kuhn et al., 2014) for testing i.i.d. and vector autoregressive moving average (VARMA) models. CLT approximations were motivated, for example, in Siegmund (1985). We compare the numerical performance of the tests under both types of limiting regimes with respect to the false alarm probability and the detection delay.

Visual Speech Recognition based on Improved type of Hidden Markov Model

This paper concerns the issue of finite-time dissipative asynchronous output feedback control for a wind turbine system that can be modelled as Markov jump Lur'e systems. Due to quantisation, time delays and ubiquitous data dropouts, the actual controller modes in practical application cannot always operate synchronously with the plant modes. To conquer this difficulty, a hidden Markov model is employed to provide some estimated modes information for controlling. In addition, by the Lyapunov function approach and linear matrix inequality technology, sufficient conditions are developed to guarantee finite-time boundedness of the continuous-time closed-loop system subject to strictly -α-dissipative performance. Finally, a simulation example of a wind turbine system is given to verify the correctness and applicability of the proposed controller.

In this paper, we consider the approximate identification problem for hidden Markov models, i.e. given a finite- valued output string generated by an unknown hidden Markov model, find an approximation of the underlying model. We propose a two-step procedure for the approximate identification problem. In the first step the underlying state sequence corresponding to the output sequence is estimated directly from the output data. In the second step the system matrices are calculated from the obtained state sequence and the given output sequence. In a simulation example the performance of our proposed method is compared with the performance of the classical Baum-Welch approach for identification of hidden Markov models.

Accurately identifying faults in rolling bearing systems by analyzing vibration signals, which are often nonstationary, is challenging. To address this issue, a new approach based on complementary ensemble empirical mode decomposition (CEEMD) and time series modeling is proposed in this paper. This approach seeks to identify faults appearing in a rolling bearing system using proper autoregressive (AR) model established from the nonstationary vibration signal. First, vibration signals measured from a rolling bearing test system with different defect conditions are decomposed into a set of intrinsic mode functions (IMFs) by means of the CEEMD method. Second, vibration signals are filtered with calculated filtering parameters. Third, the IMF which is closely correlated to the filtered signal is selected according to the correlation coefficient between the filtered signal and each IMF, and then the AR model of the selected IMF is established. Subsequently, the AR model parameters are considered as the input feature vectors, and the hidden Markov model (HMM) is used to identify the fault pattern of a rolling bearing. Experimental study performed on a bearing test system has shown that the presented approach can accurately identify faults in rolling bearings.

Packet loss in a networked control system (NCS) is a stochastic event that depends on the current network state, which, as an abstract and hidden variable, can not be well defined but can be perceived by the observation of the packet-loss process. We model the network in an NCS as a Markov chain and the packet losses as stochastic events whose probability depends on the Markov chain state (i.e. network state). Thus, the network state estimation is a hidden Markov model (HMM) estimation problem. To derive the HMM of the NCS under study, we propose the expectation maximization (EM) algorithm to estimate the parameters of the HMM based on the packet dropping sequence. Finally, a simulation example illustrates the effectiveness of the proposed approach.

International audience