Identifying the Recurrence of Sleep Apnea Using A Harmonic Hidden Markov Model.
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ABSTRACT:
SUBMITTER: Hadj-Amar B
PROVIDER: S-EPMC7611772 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description: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.
| S-EPMC2735038 | biostudies-literature
Project description:In the genomic era, group association tests are of great interest. Due to the overwhelming number of individual genomic features, the power of testing for association of a single genomic feature at a time is often very small, as are the effect sizes for most features. Many methods have been proposed to test association of a trait with a group of features within a functional unit as a whole, e.g. all SNPs in a gene, yet few of these methods account for the fact that generally a substantial proportion of the features are not associated with the trait. In this paper, we propose to model the association for each feature in the group as a mixture of features with no association and features with non-zero associations to explicitly account for the possibility that a fraction of features may not be associated with the trait while other features in the group are. The feature-level associations are first estimated by generalized linear models; the sequence of these estimated associations is then modeled by a hidden Markov chain. To test for global association, we develop a modified likelihood ratio test based on a log-likelihood function that ignores higher order dependency plus a penalty term. We derive the asymptotic distribution of the likelihood ratio test under the null hypothesis. Furthermore, we obtain the posterior probability of association for each feature, which provides evidence of feature-level association and is useful for potential follow-up studies. In simulations and data application, we show that our proposed method performs well when compared with existing group association tests especially when there are only few features associated with the outcome.
| S-EPMC4834946 | biostudies-literature
Project description:The availability of reliable genetic linkage maps is crucial for functional and evolutionary genomic analyses. Established theory and methods of genetic linkage analysis have made map construction a routine exercise in diploids. However, many evolutionarily, ecologically, and/or agronomically important species are autopolyploids, with autotetraploidy being a typical example. These species undergo much more complicated chromosomal segregation and recombination at meiosis than diploids. In addition, there is evidence of polyploidy-induced and highly dynamic changes in the structure of the genome. These polysomic characteristics indicate the inappropriateness of the theory and methods of linkage analysis in diploids for use in these species and a gap in the theory and methodology of tetraploid map construction. This paper presents a theoretical model and statistical framework for multilocus linkage analysis in autotetraploids for use with dominant and/or codominant DNA molecular markers. The theory and methods incorporate the essential features of allele segregation and recombination under tetrasomic inheritance and the major challenges in statistical modeling and marker data analysis. We validated the method and explored its statistical properties by intensive simulation study and demonstrated its utility by analysis of AFLP and SSR marker data from an outbred autotetraploid potato population.
| S-EPMC2840161 | biostudies-literature
Project description:The discovery of cis-regulatory modules (CRMs) is the key to understanding mechanisms of transcription regulation. Since CRMs have specific regulatory structures that are the basis for the regulation of gene expression, how to model the regulatory structure of CRMs has a considerable impact on the performance of CRM identification. The paper proposes a CRM discovery algorithm called ComSPS. ComSPS builds a regulatory structure model of CRMs based on HMM by exploring the rules of CRM transcriptional grammar that governs the internal motif site arrangement of CRMs. We test ComSPS on three benchmark datasets and compare it with five existing methods. Experimental results show that ComSPS performs better than them.
| S-EPMC5405574 | biostudies-literature
Project description:The progression of complex diseases is generally divided as a normal state, a pre-disease state or tipping point, and a disease state. Developing individual-specific method that can identify the pre-disease state just before a catastrophic deterioration, is critical for patients with complex diseases. However, with only a case sample, it is challenging to detect a pre-disease state which has little significant differences comparing with a normal state in terms of phenotypes and gene expressions. In this study, by regarding the tipping point as the end point of a stationary Markov process, we proposed a single-sample-based hidden Markov model (HMM) approach to explore the dynamical differences between a normal and a pre-disease states, and thus can signal the upcoming critical transition immediately after a pre-disease state. Using this method, we identified the pre-disease state or tipping point in a numerical simulation and two real datasets including stomach adenocarcinoma and influenza infection, which demonstrate the effectiveness of the method.
| S-EPMC6458292 | biostudies-literature
Project description:Electronic telemetry is frequently used to document animal movement through time. Methods that can identify underlying behaviors driving specific movement patterns can help us understand how and why animals use available space, thereby aiding conservation and management efforts. For aquatic animal tracking data with significant measurement error, a Bayesian state-space model called the first-Difference Correlated Random Walk with Switching (DCRWS) has often been used for this purpose. However, for aquatic animals, highly accurate tracking data are now becoming more common. We developed a new hidden Markov model (HMM) for identifying behavioral states from animal tracks with negligible error, called the hidden Markov movement model (HMMM). We implemented as the basis for the HMMM the process equation of the DCRWS, but we used the method of maximum likelihood and the R package TMB for rapid model fitting. The HMMM was compared to a modified version of the DCRWS for highly accurate tracks, the DCRWSNOME, and to a common HMM for animal tracks fitted with the R package moveHMM. We show that the HMMM is both accurate and suitable for multiple species by fitting it to real tracks from a grey seal, lake trout, and blue shark, as well as to simulated data. The HMMM is a fast and reliable tool for making meaningful inference from animal movement data that is ideally suited for ecologists who want to use the popular DCRWS implementation and have highly accurate tracking data. It additionally provides a groundwork for development of more complex modeling of animal movement with TMB. To facilitate its uptake, we make it available through the R package swim.
| S-EPMC5383489 | biostudies-literature
Project description:Drug-induced sleep fragmentation can cause sleep disturbances either via their intended pharmacological action or as a side effect. Examples of disturbances include excessive daytime sleepiness, insomnia and nightmares. Developing drugs without these side effects requires insight into the mechanisms leading to sleep disturbance. The characterization of the circadian sleep pattern by EEG following drug exposure has improved our understanding of these mechanisms and their translatability across species. The EEG shows frequent transitions between specific sleep states leading to multiple correlated sojourns in these states. We have developed a Markov model to consider the high correlation in the data and quantitatively compared sleep disturbance in telemetered rats induced by methylphenidate, which is known to disturb sleep, and of a new chemical entity (NCE). It was assumed that these drugs could either accelerate or decelerate the transitions between the sleep states. The difference in sleep disturbance of methylphenidate and the NCE were quantitated and different mechanisms of action on rebound sleep were identified. The estimated effect showed that both compounds induce sleep fragmentation with methylphenidate being fivefold more potent compared to the NCE.
| S-EPMC3215869 | biostudies-literature
Project description:New biological techniques and technological advances in high-throughput sequencing are paving the way for systematic, comprehensive annotation of many genomes, allowing differences between cell types or between disease/normal tissues to be determined with unprecedented breadth. Epigenetic modifications have been shown to exhibit rich diversity between cell types, correlate tightly with cell-type specific gene expression, and changes in epigenetic modifications have been implicated in several diseases. Previous attempts to understand chromatin state have focused on identifying combinations of epigenetic modification, but in cases of multiple cell types, have not considered the lineage of the cells in question.We present a Bayesian network that uses epigenetic modifications to simultaneously model 1) chromatin mark combinations that give rise to different chromatin states and 2) propensities for transitions between chromatin states through differentiation or disease progression. We apply our model to a recent dataset of histone modifications, covering nine human cell types with nine epigenetic modifications measured for each. Since exact inference in this model is intractable for all the scale of the datasets, we develop several variational approximations and explore their accuracy. Our method exhibits several desirable features including improved accuracy of inferring chromatin states, improved handling of missing data, and linear scaling with dataset size. The source code for our model is available at http:// http://github.com/uci-cbcl/tree-hmm.
| S-EPMC3622631 | biostudies-literature
Project description:Chromatin-state analysis is widely applied in the studies of development and diseases. However, existing methods operate at a single length scale, and therefore cannot distinguish large domains from isolated elements of the same type. To overcome this limitation, we present a hierarchical hidden Markov model, diHMM, to systematically annotate chromatin states at multiple length scales. We apply diHMM to analyse a public ChIP-seq data set. diHMM not only accurately captures nucleosome-level information, but identifies domain-level states that vary in nucleosome-level state composition, spatial distribution and functionality. The domain-level states recapitulate known patterns such as super-enhancers, bivalent promoters and Polycomb repressed regions, and identify additional patterns whose biological functions are not yet characterized. By integrating chromatin-state information with gene expression and Hi-C data, we identify context-dependent functions of nucleosome-level states. Thus, diHMM provides a powerful tool for investigating the role of higher-order chromatin structure in gene regulation.
| S-EPMC5385569 | biostudies-literature
Project description:In previous work, we developed a novel algorithm, VIPR, for analyzing diagnostic microarray data using a training set of empirical hybridizations of infected and uninfected samples. We have expanded up our previous implementation by incorporating a hidden Markov model (HMM) to detect recombination. We trained our HMM on a set of nonrecombinant parental viruses and applied our method to 11 recombinant alphaviruses and 4 recombinant flaviviruses hybridized to a diagnostic microarray in order to evaluate performance of the HMM. VIPR HMM identified 95% of the 62 inter-species recombinant breakpoints in the validation set and only two false positive breakpoints were predicted. This study represents the first description and validation of an algorithm capable of identifying recombination in viruses based on diagnostic microarray hybridization patterns.
2012-10-18 | GSE34490 | GEO