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Hidden Markov Models in Bioinformatics
Current Bioinformatics, 2007Hidden Markov Models (HMMs) became recently important and popular among bioinformatics researchers, and many software tools are based on them. In this survey, we first consider in some detail the mathematical foundations of HMMs, we describe the most important algorithms, and provide useful comparisons, pointing out advantages and drawbacks.
Valeria De Fonzo +2 more
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Current Opinion in Structural Biology, 1996
'Profiles' of protein structures and sequence alignments can detect subtle homologies. Profile analysis has been put on firmer mathematical ground by the introduction of hidden Markov model (HMM) methods. During the past year, applications of these powerful new HMM-based profiles have begun to appear in the fields of protein-structure prediction and ...
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'Profiles' of protein structures and sequence alignments can detect subtle homologies. Profile analysis has been put on firmer mathematical ground by the introduction of hidden Markov model (HMM) methods. During the past year, applications of these powerful new HMM-based profiles have begun to appear in the fields of protein-structure prediction and ...
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2019
Hidden Markov models are tractable to capture long-term dependencies but intractable to compute the transition probabilities of higher-order process. We propose a neural hidden Markov models to compute the transition probabilities of higher-order hidden Markov model by a neural network and reduce the cost of computation.
Zuoquan Lin, Jiehu Song
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Hidden Markov models are tractable to capture long-term dependencies but intractable to compute the transition probabilities of higher-order process. We propose a neural hidden Markov models to compute the transition probabilities of higher-order hidden Markov model by a neural network and reduce the cost of computation.
Zuoquan Lin, Jiehu Song
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On the structure of hidden Markov models
Pattern Recognition Letters, 2004This paper investigates the effect of HMM structure on the performance of HMM-based classifiers. The investigation is based on the framework of graphical models, the diffusion of credits of HMMs and empirical experiments. Although some researchers have focused on determining the number of states, this study shows that the topology has a stronger ...
Karim T. Abou-Moustafa +2 more
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Filtering on hidden Markov models
IEEE Signal Processing Letters, 2000In this letter, we propose a novel approach to adapt the hidden Markov model (HMM) parameters when the original feature vector sequences are transformed by a causal finite impulse response (FIR) filter. Our approach enables us to be free from the requirement of retraining the whole recognition parameters when the feature vectors are changed and makes ...
Nam Soo Kim, Dong Kook Kim
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Linguistic hidden Markov models
The 12th IEEE International Conference on Fuzzy Systems, 2003. FUZZ '03., 2004In this paper we develop a hidden Markov model (HMM), called the linguistic HMM (LHMM), suitable for processing sequences of fuzzy vectors. A fuzzy vector B is an n-tuple of fuzzy numbers. Since fuzzy numbers are often associated with linguistic terms, such as "small," "medium," etc., a fuzzy vector can also be called a linguistic vector.
Mihail Popescu +2 more
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A Hidden Markov Model for Seismocardiography
IEEE Transactions on Biomedical Engineering, 2017We propose a hidden Markov model approach for processing seismocardiograms. The seismocardiogram morphology is learned using the expectation-maximization algorithm, and the state of the heart at a given time instant is estimated by the Viterbi algorithm. From the obtained Viterbi sequence, it is then straightforward to estimate instantaneous heart rate,
Johan Wahlström +6 more
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Hidden Markov Model with Markovian emission
Monte Carlo Methods and Applications, 2020Abstract In our paper [A. Nasroallah and K. Elkimakh, HMM with emission process resulting from a special combination of independent Markovian emissions, Monte Carlo Methods Appl. 23 2017, 4, 287–306] we have studied, in a first scenario, the three fundamental hidden Markov problems assuming that, given the hidden process, the observed ...
Karima Elkimakh, Abdelaziz Nasroallah
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Hidden Markov Models in Biology
2009Markov and Hidden Markov models (HMMs) are introduced using examples from linkage mapping and sequence analysis. In the course, the forward-backward, the Viterbi, the Baum-Welch (EM) algorithm, and a Metropolis sampling scheme are presented.
Vogl, Claus, Futschik, Andreas
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A modified hidden Markov model
Automatica, 2013This paper considers two discrete time, finite state processes X and Y. In the usual hidden Markov model X modulates the values of Y. However, the values of Y are then i.i.d. given X. In this paper a new model is considered where the Markov chain X modulates the transition probabilities of the second, observed chain Y.
John van der Hoek, Robert J. Elliott
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