Simple Hidden Markov Model Research Articles

Single-shot number-resolved detection of microwave photons with error mitigation

Single-photon detectors are ubiquitous and integral components of photonic quantum cryptography, communication, and computation. Many applications, however, require not only detecting the presence of any photons, but distinguishing the number present with a single shot. Here, we implement a single-shot, high-fidelity photon number-resolving detector of up to 15 microwave photons in a cavity-qubit circuit QED platform. This detector functions by measuring a series of generalized parity operators which make up the bits in the binary decomposition of the photon number. Our protocol consists of successive, independent measurements of each bit by entangling the ancilla with the cavity, then reading out and resetting the ancilla. Photon loss and ancilla readout errors can flip one or more bits, causing nontrivial errors in the outcome, but these errors have a traceable form which can be captured in a simple hidden Markov model. Relying on the independence of each bit measurement, we mitigate biases in ensembles of measurements, showing good agreement with the predictions of the model. The mitigation improves the average total variation distance error of Fock states from $13.5\%$ to $1.1\%$. We also show that the mitigation is efficiently scalable to an $M$-mode system provided that the errors are independent and sufficiently small. Our work motivates the development of new algorithms that utilize single-shot, high-fidelity PNR detectors.

Markov Model for HF Joint Spectrum Occupancy at Two Distant Locations

Abstract Even with the present-day development of mobile communications in UHF and SHF spectral bands and recent trends to move to mmWave, HF radio communications still offer advantages that make them exclusive in some application areas. Cognitive Radio principles can be applied to HF communications, to make use of the spectrum more efficiently. A significant improvement of Cognitive Radio technique can be achieved by spectrum occupancy prediction, which enables proactive efficient spectrum utilization. Hidden Markov Models (HMM) can be a useful tool for statistical spectrum occupancy prediction. In some of our previous works, we investigated a simple HMM in time domain, as well as a bidimensional model, that take into account both the time domain and the frequency domain. In this paper, we propose to extend the coverage of the model, by considering in addition to time and frequency the geographical position of the two points involved in the radio connection. The work is motivated by the fact that the two points of the radio link in HF can be far apart, and the channel availability at the two points could be different. Therefore, finding and exploiting correlations between the spectrum occupancy at the two points might improve the connectivity. The proposed model is validated using real spectrum occupancy data collected simultaneously at two locations situated in two cities, namely Timișoara and Sibiu, Romania. The measurements have been made using two identical USRP equipment, GPS coordinated, and identical HF broadband antennas.

A novel method for day-ahead solar power prediction based on hidden Markov model and cosine similarity

Nowadays, with the emergence of new technologies such as smart grid and increasing the use of renewable energy in the grid, energy prediction has become more important in the electricity industry. Furthermore, with growing the integration of power generated from renewable energy sources into grids, an accurate forecasting tool for the reduction in undesirable effects of this scenario is essential. This study has developed a novel approach based on the hidden Markov model (HMM) for forecasting day-ahead solar power. The aim is to find a pattern of solar power changes at a given time in consecutive days. The proposed approach consists of two steps. In the first step, the cosine similarity is used to determine the similarity of solar power variations on consecutive days to a particular vector. In the second step, the obtained information from the first step is fed to HMM as a feature vector. These data are used for training and forecasting day-ahead solar power. After obtaining the preliminary results of the prediction, two known filters are utilized as post-processing to remove spikes and smooth the results. Finally, the performance of the proposed method is tested on real NREL data. No meteorological data (even solar radiation) are used; moreover, the model is fed only from the solar power of the past 23 days. To evaluate the proposed method, a feed-forward neural network and a simple HMM are examined with the same data and conditions. All three methods are tested with and without the post-processing. The results show that the proposed model is superior to other examined methods in terms of accuracy and computational time.

Supervised Learning for Automated Infectious-Disease-Outbreak Detection

ObjectiveBy systematically scoring algorithms and integrating outbreak data through statistical learning, evaluate and improve the performance of automated infectious-disease-outbreak detection. The improvements should be directly relevant to the epidemiological practice. A broader objective is to explore the usefulness of machine-learning approaches in epidemiology.IntroductionWithin the traditional surveillance of notifiable infectious diseases in Germany, not only are individual cases reported to the Robert Koch Institute, but also outbreaks themselves are recorded: A label is assigned by epidemiologists to each case, indicating whether it is part of an outbreak and of which. This expert knowledge represents, in the language of machine leaning, a "ground truth" for the algorithmic task of detecting outbreaks from a stream of surveillance data. The integration of this kind of information in the design and evaluation of algorithms is called supervised learning.MethodsReported cases were aggregated weekly and divided into two count time series, one for endemic (not part of an outbreak) and one for epidemic cases. Two new algorithms were developed for the analysis of such time series: farringtonOutbreak is an adaptation of the standard method farringtonFlexible as implemented in the surveillance R package: It trains on endemic case counts but detects anomalies on total case counts. The second algorithm is hmmOutbreak, which is based on a hidden Markov model (HMM): A binary hidden state indicates whether an outbreak was reported in a given week, the transition matrix for this state is learned from the outbreak data and this state is integrated as factor in a generalised linear model of the total case count. An explicit probability of being in a state of outbreak is then computed for each week (one-week ahead) and a signal is generated if it is higher than a user-defined threshold.To evaluate performance, we framed outbreak detection as a simple binary classification problem: Is there an outbreak in a given week, yes or no? Was a signal generated for this week, yes or no? One can thus count, for each time series, the true positives (outbreak data and signals agree), false positives, true negatives and false negatives. From those, classical performance scores can be computed, such as sensitivity, specificity, precision, F-score or area under the ROC curve (AUC).For the evaluation with real-word data we used time series of reported cases of salmonellosis and campylobacteriosis for each of the 412 German counties over 9 years. We also ran simple simulations with different parameter sets, generating count time series and outbreaks with the sim.pointSource function of the surveillance R package.ResultsWe have developed a supervised-learning framework for outbreak detection based on reported infections and outbreaks, proposing two algorithms and an evaluation method. hmmOutbreak performs overall much better than the standard farringtonFlexible, with e.g. a 60% improvement in sensitivity (0.5 compared to 0.3) at a fixed specificity of 0.9. The results were confirmed by simulations. Furthermore, the computation of explicit outbreak probabilities allows a better and clearer interpretation of detection results than the usual testing of the null hypothesis "is endemic".ConclusionsMethods of machine learning can be usefully applied in the context of infectious-disease surveillance. Already a simple HMM shows large improvements and better interpretability: More refined methods, in particular semi-supervised approaches, look thus very promising. The systematic integration of available expert knowledge, in this case the recording of outbreaks, allows an evaluation of algorithmic performance that is of direct relevance for the epidemiological practice, in contrast to the usual intrinsic statistical metrics. Beyond that, this knowledge can be readily used to improve that performance and, in the future, gain insights in outbreak dynamics. Moreover, other types of labels will be similarly integrated in automated surveillance analyses, e.g. user feedback on whether a signal was relevant (reinforcement learning) or messages on specialised internet platforms that were found to be useful warnings of international epidemic events.

Regime switching model estimation: spectral clustering hidden Markov model

We propose a novel method for Markov regime switching (MRS) model estimations by spectral clustering hidden Markov model (SC-HMM). The proposed SC-HMM exploits the Markov property of hidden states and utilizes pairwise feature similarities for latent state identifications. It can be applied to general hidden Markov models (HMMs) with continuous observations. In contrast to the maximum likelihood estimation (MLE), SC-HMM predicts latent states and yields conditional distribution statistics without knowledge of types of conditional distributions. To illustrate, SC-HMM is first applied to a simple HMM with discrete observations. We consider the MRS model estimation with continuous observations to further demonstrate SC-HMM. Specifically, based on local observations, we propose a set of features for the MRS estimation. A similarity matrix is determined from derived features and spectral clustering predicts latent states. Conditional distribution statistics and transitional probabilities are estimated based on identified latent states. By conducting simulation studies on both two-state and three-state MRS estimations, we demonstrate that, in comparison with MLE, the proposed SC-HMM is more robust. Furthermore, we demonstrate the validity of SC-HMM by estimating a two-state MRS from the S&P/TSX Composite Index daily and monthly data from 1977 to 2014.

A Nonstationary Hidden Markov Model with Approximately Infinitely-Long Time-Dependencies

Hidden Markov models (HMMs) are a popular approach for modeling sequential data, typically based on the assumption of a first-order Markov chain. In other words, only one-step back dependencies are modeled which is a rather unrealistic assumption in most applications. In this paper, we propose a method for postulating HMMs with approximately infinitely-long time-dependencies. Our approach considers the whole history of model states in the postulated dependencies, by making use of a recently proposed nonparametric Bayesian method for modeling label sequences with infinitely-long time dependencies, namely the sequence memoizer. We manage to derive training and inference algorithms for our model with computational costs identical to simple first-order HMMs, despite its entailed infinitely-long time-dependencies, by employing a mean-field-like approximation. The efficacy of our proposed model is experimentally demonstrated.

PREVENTER, a Selection Mechanism for Just-in-Time Preventive Interventions

This paper examines just-in-time adaptive interventions (JITAIs) for stress, a pervasive and affective computing application with significant implications for long-term health and quality of life. We discuss fundamental components needed to enabling JITAIs based for one kind of affect data stress. Chronic stress has significant long-term behavioral and physical health consequences, including an increased risk of cardiovascular disease, cancer, anxiety and depression. This paper conducts post-hoc experiments and simulations to demonstrate feasibility of both real-time stress forecasting and stress intervention adaptation and optimization. Using physiological data collected by ten individuals in the natural environment for one week, we show 1) that simple Hidden Markov Models (HMMs) can be used to forecast physiological measures of stress with up to 3 minutes in advance; and 2) Q-Learning (QL) combined with eligibility traces could be used by an affective computing system to adapt and deliver any number and type of interventions in response to changes in affect. Our hope is that this work will take us one step closer to using pervasive devices to assist in the daily management of chronic stress and other affect-related challenges.

Gesture recognition based on HMM-FNN model using a Kinect

Addressing the problem of complex dynamic gesture recognition, this paper obtains the body depth image through the body feeling sensor device—Kinect; the threshold segmentation method is used to segment the gestures depth image, on the basis of the common distance between hand and body. Then, the HMM-FNN model, which combines the hidden markov model (HMM) and the fuzzy neural network (FNN), is used for dynamic gesture recognition. This paper mainly focuses on the trainees’ common operations of equipment in virtual substation to set the custom gesture interaction sets. Based on the characteristic of the complex dynamic gesture, gesture image was decomposed into three feature sequences—hand shape change, hand position changes in the two-dimensional plane, and movement in the Z-axis direction, for feature extraction. The HMM model is respectively built according to the three sub sequences, and the FNN was connected to judge the semantics of gesture using the fuzzy reasoning. By experimental verification, the HMM-FNN model can quickly and effectively identify complicated dynamic hand gestures. Meanwhile, it has strong robustness. The recognition effect is superior to that of the simple HMM model.

Hybrid Approach for Named Entity Recognition

This paper proposes the Named Entity Recognition (NER) system for Punjabi language using a hybrid approach in which rule based approach and machine learning approach i.e. Hidden Markov Model (HMM) is combined. With no Dataset available, the Named Entities (NEs) were manually tagged which led us to the creation of training and testing dataset, under the linguistic supervision. Using hybrid approach, the proposed system is able to recognize Name of person, Location, Time, Date, Designation, Organization, Titleperson, Event, Abbreviation, Facility, Number, Artifact, Relation and Measure. This paper presents two versions of NER for Punjabi language, the first version is designed with HMM only and the second version is designed hybrid approach in which HMM is used in combination with handcrafted rules. NER system with proposed hybrid approach is able to achieve the precision of 72.92%, Recall of 76.27%, F-measure of 74.56% with hybrid approach and Precision, Recall and F-measure of 47.57%, 48.98%, 48.27% respectively has been achieved by using HMM only. This paper has also compared proposed method with simple HMM and observed that proposed NER system performs better. General Terms Named Entity recognition, Natural Language Processing (NLP), Hybrid approach.

A two-level Markov model for packet loss in UDP/IP-based real-time video applications targeting residential users

The packet loss characteristics of Internet paths that include residential broadband links are not well understood, and there are no good models for their behaviour. This complicates the design of real-time video applications targeting home users, since it is difficult to choose appropriate error correction and concealment algorithms without a good model for the types of loss observed. Using measurements of residential broadband networks in the UK and Finland, we show that existing models for packet loss, such as the Gilbert model and simple hidden Markov models, do not effectively model the loss patterns seen in this environment. We present a new two-level Markov model for packet loss that can more accurately describe the characteristics of these links, and quantify the effectiveness of this model. We demonstrate that our new packet loss model allows for improved application design, by using it to model the performance of forward error correction on such links.

Fall-Detection Algorithm Using 3-Axis Acceleration: Combination with Simple Threshold and Hidden Markov Model

Falls are a serious medical and social problem among the elderly. This has led to the development of automatic fall-detection systems. To detect falls, a fall-detection algorithm that combines a simple threshold method and hidden Markov model (HMM) using 3-axis acceleration is proposed. To apply the proposed fall-detection algorithm and detect falls, a wearable fall-detection device has been designed and produced. Several fall-feature parameters of 3-axis acceleration are introduced and applied to a simple threshold method. Possible falls are chosen through the simple threshold and are applied to two types of HMM to distinguish between a fall and an activity of daily living (ADL). The results using the simple threshold, HMM, and combination of the simple method and HMM were compared and analyzed. The combination of the simple threshold method and HMM reduced the complexity of the hardware and the proposed algorithm exhibited higher accuracy than that of the simple threshold method.

Hidden Markov Models for the Stimulus-Response Relationships of Multistate Neural Systems

Given recent experimental results suggesting that neural circuits may evolve through multiple firing states, we develop a framework for estimating state-dependent neural response properties from spike train data. We modify the traditional hidden Markov model (HMM) framework to incorporate stimulus-driven, non-Poisson point-process observations. For maximal flexibility, we allow external, time-varying stimuli and the neurons' own spike histories to drive both the spiking behavior in each state and the transitioning behavior between states. We employ an appropriately modified expectation-maximization algorithm to estimate the model parameters. The expectation step is solved by the standard forward-backward algorithm for HMMs. The maximization step reduces to a set of separable concave optimization problems if the model is restricted slightly. We first test our algorithm on simulated data and are able to fully recover the parameters used to generate the data and accurately recapitulate the sequence of hidden states. We then apply our algorithm to a recently published data set in which the observed neuronal ensembles displayed multistate behavior and show that inclusion of spike history information significantly improves the fit of the model. Additionally, we show that a simple reformulation of the state space of the underlying Markov chain allows us to implement a hybrid half-multistate, half-histogram model that may be more appropriate for capturing the complexity of certain data sets than either a simple HMM or a simple peristimulus time histogram model alone.

A hidden Markov model to predict early mastitis from test-day somatic cell scores

In many countries, high somatic cell scores (SCS) in milk are used as an indicator for mastitis because they are collected on a routine basis. However, individual test-day SCS are not very accurate in identifying infected cows. Mathematical models may improve the accuracy of the biological marker by making better use of the information contained in the available data. Here, a simple hidden Markov model (HMM) is described mathematically and applied to SCS recorded monthly on cows with or without clinical mastitis to evaluate its accuracy in estimating parameters (mean, variance and transition probabilities) under healthy or diseased states. The SCS means were estimated at 1.96 (s.d. = 0.16) and 4.73 (s.d. = 0.71) for the hidden healthy and infected states, and the common variance at 0.83 (s.d. = 0.11). The probability of remaining uninfected, recovering from infection, getting newly infected and remaining infected between consecutive test days was estimated at 78.84%, 60.49%, 11.70% and 15%, respectively. Three different health-related states were compared: clinical stages observed by farmers, subclinical cases defined for somatic cell counts below or above 250 000 cells/ml and infected stages obtained from the HMM. The results showed that HMM identifies infected cows before the appearance of clinical and subclinical signs, which may critically improve the power of the studies on the genetic determinants of SCS and reduce biases in predicting breeding values for SCS.

A hidden Markov model for earthquake declustering

The hidden Markov model (HMM) and related algorithms provide a powerful framework for statistical inference on partially observed stochastic processes. HMMs have been successfully implemented in many disciplines, though not as widely applied as they should be in earthquake modeling. In this article, a simple HMM earthquake occurrence model is proposed. Its performance in declustering is compared with the epidemic‐type aftershock sequence model, using a data set of the central and western regions of Japan. The earthquake clusters and the single earthquakes separated using our model show some interesting geophysical differences. In particular, the log‐linear Gutenberg‐Richter frequency‐magnitude law (G‐R law) for the earthquake clusters is significantly different from that for the single earthquakes.

Hidden Markov models for the stimulus-response relationships of multi-state neural systems

Event Abstract Back to Event Hidden Markov models for the stimulus-response relationships of multi-state neural systems Sean Escola1* and Liam Paninski2 1 Columbia University, Center for Theoretical Neuroscience, United States 2 Columbia University, United States Recent experimental results suggest that neural networks are associated with multiple firing regimes, or states, such as tonic and burst modes in thalamus (for review, see Sherman, Trends in Neuroscience, 2001) and UP and DOWN states in cortex (e.g. Anderson et al., Nature Neuroscience, 2000). It is reasonable to speculate that neurons in multi-state networks that are involved in sensory processing might display differential firing behaviors in response to the same stimulus in each of the states of the system, and, indeed, Bezdudnaya et al. (Neuron, 2006) showed that temporal receptive field properties change between tonic and burst states for relay cells in rabbit thalamus. Motivated by these results, we previously presented a general framework for estimating state-dependent neural response properties from paired spike-train and stimulus data assuming that neuronal assemblies transition between several discrete hidden states (Escola and Paninski, Cosyne, 2007). We modified the traditional hidden Markov model (HMM) framework to permit point-process observables such as spike-trains, and, for maximal flexibility in our model, we allowed an external, time-varying stimulus, if present, and the neurons’ own spike histories to drive both the spiking behavior in each state and the transitioning behavior between states. We showed that an appropriately modified expectation-maximization algorithm could be constructed to learn the model parameters and gave preliminary results with simulated data. Although HMMs have been used previously to analyze neuronal data (e.g. Abeles et al., Proceedings of the National Academy of Sciences, 1995; Chen et al., Neural Computation, 2009), our model is an extension to the stimulus and history-dependent regime. In this poster, we review this previous work and then apply our model to a recently published data set of known multi-state neuronal ensembles (Jones et al., Proceedings of the National Academy of Sciences, 2007). We show that inclusion of spike-history information significantly improves the fit of the model compared to the analysis given in Jones et al. We then show that a simple reformulation of the state-space of the HMM’s underlying Markov chain allows us to implement a hybrid half-multi-state/half-histogram model which captures more of the neuronal variability than either a simple HMM or a simple peri-stimulus time histogram (PSTH) model alone. This hybrid model learns firing-rate histograms that are triggered by the state-transition times rather than the trial start-times (i.e. these are state-dependent peri-transition time histograms or PTTHs as opposed to traditional PSTHs), and uncovers interesting and unexpected transition-locked dynamics in the data such as oscillations that are phase-locked to the transition times. We believe that techniques such as these may allow for the identification of data as multi-state that could not have been so identified by earlier methods, particularly data derived from neural systems where it is the network dynamics that are state-dependent rather than simple features such as firing rate, inter-spike interval distribution, or resting membrane potential. Conference: Computational and Systems Neuroscience 2010, Salt Lake City, UT, United States, 25 Feb - 2 Mar, 2010. Presentation Type: Poster Presentation Topic: Poster session III Citation: Escola S and Paninski L (2010). Hidden Markov models for the stimulus-response relationships of multi-state neural systems. Front. Neurosci. Conference Abstract: Computational and Systems Neuroscience 2010. doi: 10.3389/conf.fnins.2010.03.00237 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 04 Mar 2010; Published Online: 04 Mar 2010. * Correspondence: Sean Escola, Columbia University, Center for Theoretical Neuroscience, New York, United States, gse3@columbia.edu Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Sean Escola Liam Paninski Google Sean Escola Liam Paninski Google Scholar Sean Escola Liam Paninski PubMed Sean Escola Liam Paninski Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

Disease surveillance using a hidden Markov model

BackgroundRoutine surveillance of disease notification data can enable the early detection of localised disease outbreaks. Although hidden Markov models (HMMs) have been recognised as an appropriate method to model disease surveillance data, they have been rarely applied in public health practice. We aimed to develop and evaluate a simple flexible HMM for disease surveillance which is suitable for use with sparse small area count data and requires little baseline data.MethodsA Bayesian HMM was designed to monitor routinely collected notifiable disease data that are aggregated by residential postcode. Semi-synthetic data were used to evaluate the algorithm and compare outbreak detection performance with the established Early Aberration Reporting System (EARS) algorithms and a negative binomial cusum.ResultsAlgorithm performance varied according to the desired false alarm rate for surveillance. At false alarm rates around 0.05, the cusum-based algorithms provided the best overall outbreak detection performance, having similar sensitivity to the HMMs and a shorter average time to detection. At false alarm rates around 0.01, the HMM algorithms provided the best overall outbreak detection performance, having higher sensitivity than the cusum-based Methods and a generally shorter time to detection for larger outbreaks. Overall, the 14-day HMM had a significantly greater area under the receiver operator characteristic curve than the EARS C3 and 7-day negative binomial cusum algorithms.ConclusionOur findings suggest that the HMM provides an effective method for the surveillance of sparse small area notifiable disease data at low false alarm rates. Further investigations are required to evaluation algorithm performance across other diseases and surveillance contexts.

Nucleosome positioning from tiling microarray data

Motivation: The packaging of DNA around nucleosomes in eukaryotic cells plays a crucial role in regulation of gene expression, and other DNA-related processes. To better understand the regulatory role of nucleosomes, it is important to pinpoint their position in a high (5–10 bp) resolution. Toward this end, several recent works used dense tiling arrays to map nucleosomes in a high-throughput manner. These data were then parsed and hand-curated, and the positions of nucleosomes were assessed.Results: In this manuscript, we present a fully automated algorithm to analyze such data and predict the exact location of nucleosomes. We introduce a method, based on a probabilistic graphical model, to increase the resolution of our predictions even beyond that of the microarray used. We show how to build such a model and how to compile it into a simple Hidden Markov Model, allowing for a fast and accurate inference of nucleosome positions.We applied our model to nucleosomal data from mid-log yeast cells reported by Yuan et al. and compared our predictions to those of the original paper; to a more recent method that uses five times denser tiling arrays as explained by Lee et al.; and to a curated set of literature-based nucleosome positions. Our results suggest that by applying our algorithm to the same data used by Yuan et al. our fully automated model traced 13% more nucleosomes, and increased the overall accuracy by about 20%. We believe that such an improvement opens the way for a better understanding of the regulatory mechanisms controlling gene expression, and how they are encoded in the DNA.Contact: nir@cs.huji.ac.il

Hidden Markov models for the complex stimulus-response relationships of multi-state neurons

Event Abstract Back to Event Hidden Markov models for the complex stimulus-response relationships of multi-state neurons Sean Escola1* and Liam Paninski1 1 Columbia University, United States Recent experimental results suggest that neurons are associated with multiple firing regimes, or states. We develop a general framework for estimating state-dependent neural receptive fields from paired spike-train and stimulus data assuming that neurons transition between several discrete hidden states. We modify both the discrete-time and continuous-time traditional hidden Markov model (HMM) frameworks to permit point-process observables such as spike-trains. For maximal flexibility in our model, we allow the external, time-varying stimulus and the neuron's own spike-history to drive both the spiking behavior in each state and the transitioning behavior between states. We employ an appropriately modified expectation-maximization algorithm to learn the model parameters. The expectation step is solved by the standard forward-backward algorithm for HMMs. The maximization step, though it is not analytically tractable, reduces to a simple concave optimization problem if the model is restricted slightly. We test our algorithm on two stimulated data sets and are able both to fully recover the parameters used to generate the data and to accurately recapitulate the sequence of hidden states. As a demonstration of the flexibility of our model, we apply our framework to a recently-published data set of purportedly multi-state neurons and show that we can implement a hybrid half multi-state/half histogram model which captures more of the neuronal variability than either a simple HMM or a simple histogram-based model alone Conference: Bernstein Symposium 2008, Munich, Germany, 8 Oct - 10 Oct, 2008. Presentation Type: Poster Presentation Topic: All Abstracts Citation: Escola S and Paninski L (2008). Hidden Markov models for the complex stimulus-response relationships of multi-state neurons. Front. Comput. Neurosci. Conference Abstract: Bernstein Symposium 2008. doi: 10.3389/conf.neuro.10.2008.01.043 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 13 Nov 2008; Published Online: 13 Nov 2008. * Correspondence: Sean Escola, Columbia University, New York, United States, gse3@columbia.edu Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Sean Escola Liam Paninski Google Sean Escola Liam Paninski Google Scholar Sean Escola Liam Paninski PubMed Sean Escola Liam Paninski Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

A population study of the minicircles in Trypanosoma cruzi: predicting guide RNAs in the absence of empirical RNA editing.

BackgroundThe structurally complex network of minicircles and maxicircles comprising the mitochondrial DNA of kinetoplastids mirrors the complexity of the RNA editing process that is required for faithful expression of encrypted maxicircle genes. Although a few of the guide RNAs that direct this editing process have been discovered on maxicircles, guide RNAs are mostly found on the minicircles. The nuclear and maxicircle genomes have been sequenced and assembled for Trypanosoma cruzi, the causative agent of Chagas disease, however the complement of 1.4-kb minicircles, carrying four guide RNA genes per molecule in this parasite, has been less thoroughly characterised.ResultsFifty-four CL Brener and 53 Esmeraldo strain minicircle sequence reads were extracted from T. cruzi whole genome shotgun sequencing data. With these sequences and all published T. cruzi minicircle sequences, 108 unique guide RNAs from all known T. cruzi minicircle sequences and two guide RNAs from the CL Brener maxicircle were predicted using a local alignment algorithm and mapped onto predicted or experimentally determined sequences of edited maxicircle open reading frames. For half of the sequences no statistically significant guide RNA could be assigned. Likely positions of these unidentified gRNAs in T. cruzi minicircle sequences are estimated using a simple Hidden Markov Model. With the local alignment predictions as a standard, the HMM had an ~85% chance of correctly identifying at least 20 nucleotides of guide RNA from a given minicircle sequence. Inter-minicircle recombination was documented. Variable regions contain species-specific areas of distinct nucleotide preference. Two maxicircle guide RNA genes were found.ConclusionThe identification of new minicircle sequences and the further characterization of all published minicircles are presented, including the first observation of recombination between minicircles. Extrapolation suggests a level of 4% recombinants in the population, supporting a relatively high recombination rate that may serve to minimize the persistence of gRNA pseudogenes. Characteristic nucleotide preferences observed within variable regions provide potential clues regarding the transcription and maturation of T. cruzi guide RNAs. Based on these preferences, a method of predicting T. cruzi guide RNAs using only primary minicircle sequence data was created.

A markovian approach for the prediction of mouse isochores

Hidden Markov models (HMMs) are effective tools to detect series of statistically homogeneous structures, but they are not well suited to analyse complex structures. For example, the duration of stay in a state of a HMM must follow a geometric law. Numerous other methodological difficulties are encountered when using HMMs to segregate genes from transposons or retroviruses, or to determine the isochore classes of genes. The aim of this paper is to analyse these methodological difficulties, and to suggest new tools for the exploration of genome data. We show that HMMs can be used to analyse complex gene structures with bell-shaped length distribution by using convolution of geometric distributions. Thus, we have introduced macros-states to model the distributions of the lengths of the regions. Our study shows that simple HMM could be used to model the isochore organisation of the mouse genome. This potential use of markovian models to help in data exploration has been underestimated until now.