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- Task-Evoked Dynamic Network Analysis Through Hidden Markov Modeling
- Hidden Markov Models and their Applications in Biological Sequence Analysis
- Hidden Markov Models
Hidden Markov models HMMs have been extensively used in biological sequence analysis. In this paper, we give a tutorial review of HMMs and their applications in a variety of problems in molecular biology. We show how these HMMs can be used to solve various sequence analysis problems, such as pairwise and multiple sequence alignments, gene annotation, classification, similarity search, and many others. The successful completion of many genome sequencing projects has left us with an enormous amount of sequence data. The sequenced genomes contain a wealth of invaluable information that can help us better understand the underlying mechanisms of various biological functions in cells.
Task-Evoked Dynamic Network Analysis Through Hidden Markov Modeling
The hidden Markov models are statistical models used in many real-world applications and communities. The use of hidden Markov models has become predominant in the last decades, as evidenced by a large number of published papers. The authors evaluate the literature based on hidden Markov model variants that have been applied to various application fields. The paper represents a short but comprehensive description of research on hidden Markov model and its variants for various applications. The paper shows the significant trends in the research on hidden Markov model variants and their applications. This is a preview of subscription content, access via your institution.
View all 25 Articles. Complex thought and behavior arise through dynamic recruitment of large-scale brain networks. The signatures of this process may be observable in electrophysiological data; yet robust modeling of rapidly changing functional network structure on rapid cognitive timescales remains a considerable challenge. Here, we present one potential solution using Hidden Markov Models HMMs , which are able to identify brain states characterized by engaging distinct functional networks that reoccur over time. We show how the HMM can be inferred on continuous, parcellated source-space Magnetoencephalography MEG task data in an unsupervised manner, without any knowledge of the task timings. We apply this to a freely available MEG dataset in which participants completed a face perception task, and reveal task-dependent HMM states that represent whole-brain dynamic networks transiently bursting at millisecond time scales as cognition unfolds. The analysis pipeline demonstrates a general way in which the HMM can be used to do a statistically valid whole-brain, group-level task analysis on MEG task data, which could be readily adapted to a wide range of task-based studies.
Hidden Markov Models and their Applications in Biological Sequence Analysis
Skip to search form Skip to main content You are currently offline. Some features of the site may not work correctly. DOI: Fraser Published Mathematics. Preface 1. Introduction 2. Basic algorithms 3.
Skip to Main Content. A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity. Use of this web site signifies your agreement to the terms and conditions. Hidden Markov models and neural networks for fault detection in dynamic systems Abstract: It is shown how both pattern recognition methods in the form of neural networks and hidden Markov models HMMs can be used to automatically monitor online data for fault detection purposes. Monitoring for anomalies or faults poses some technical problems which are not normally encountered in typical HMM applications such as speed recognition.
Bhattacharya, C. August 5, Letters Dyn. April ; 1 2 : Chaotic dynamical systems are essentially nonlinear and are highly sensitive to variations in initial conditions and process parameters.
Hidden Markov Models
Bhattacharya, C. August 5, Letters Dyn.
In this model, an observation X t at time tis produced by a stochastic process, but the state Z tof this process cannot be directly observed, i. Hidden Markov models HMMs are one of the most popular methods in machine learning and statistics for modelling sequences such as speech and proteins. A system for which eq. In a Hidden Markov Model HMM , we have an invisible Markov chain which we cannot observe , and each state generates in random one out of k observations, which are visible to us..
Hidden Markov Models: Methods and Protocols guides readers through chapters on biological systems; ranging from single biomolecule, cellular level, and to organism level and the use of HMMs in unravelling the complex mechanisms that govern these complex systems. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible laboratory protocols, and tips on troubleshooting and avoiding known pitfalls. Skip to main content Skip to table of contents.
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