A new method for inferring hidden markov models from noisy time sequences.

We present a new method for inferring hidden Markov models from noisy time sequences without the necessity of assuming a model architecture, thus allowing for the detection of degenerate states. This is based on the statistical prediction techniques developed by Crutchfield et al. and generates so c...

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Autores principales:	David Kelly, Mark Dillingham, Andrew Hudson, Karoline Wiesner
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2012
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/678cd2a1d61d4313a2778f8a28d08f83
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id	oai:doaj.org-article:678cd2a1d61d4313a2778f8a28d08f83
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spelling	oai:doaj.org-article:678cd2a1d61d4313a2778f8a28d08f832021-11-18T07:30:31ZA new method for inferring hidden markov models from noisy time sequences.1932-620310.1371/journal.pone.0029703https://doaj.org/article/678cd2a1d61d4313a2778f8a28d08f832012-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/22247783/?tool=EBIhttps://doaj.org/toc/1932-6203We present a new method for inferring hidden Markov models from noisy time sequences without the necessity of assuming a model architecture, thus allowing for the detection of degenerate states. This is based on the statistical prediction techniques developed by Crutchfield et al. and generates so called causal state models, equivalent in structure to hidden Markov models. The new method is applicable to any continuous data which clusters around discrete values and exhibits multiple transitions between these values such as tethered particle motion data or Fluorescence Resonance Energy Transfer (FRET) spectra. The algorithms developed have been shown to perform well on simulated data, demonstrating the ability to recover the model used to generate the data under high noise, sparse data conditions and the ability to infer the existence of degenerate states. They have also been applied to new experimental FRET data of Holliday Junction dynamics, extracting the expected two state model and providing values for the transition rates in good agreement with previous results and with results obtained using existing maximum likelihood based methods. The method differs markedly from previous Markov-model reconstructions in being able to uncover truly hidden states.David KellyMark DillinghamAndrew HudsonKaroline WiesnerPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 7, Iss 1, p e29703 (2012)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q David Kelly Mark Dillingham Andrew Hudson Karoline Wiesner A new method for inferring hidden markov models from noisy time sequences.
description	We present a new method for inferring hidden Markov models from noisy time sequences without the necessity of assuming a model architecture, thus allowing for the detection of degenerate states. This is based on the statistical prediction techniques developed by Crutchfield et al. and generates so called causal state models, equivalent in structure to hidden Markov models. The new method is applicable to any continuous data which clusters around discrete values and exhibits multiple transitions between these values such as tethered particle motion data or Fluorescence Resonance Energy Transfer (FRET) spectra. The algorithms developed have been shown to perform well on simulated data, demonstrating the ability to recover the model used to generate the data under high noise, sparse data conditions and the ability to infer the existence of degenerate states. They have also been applied to new experimental FRET data of Holliday Junction dynamics, extracting the expected two state model and providing values for the transition rates in good agreement with previous results and with results obtained using existing maximum likelihood based methods. The method differs markedly from previous Markov-model reconstructions in being able to uncover truly hidden states.
format	article
author	David Kelly Mark Dillingham Andrew Hudson Karoline Wiesner
author_facet	David Kelly Mark Dillingham Andrew Hudson Karoline Wiesner
author_sort	David Kelly
title	A new method for inferring hidden markov models from noisy time sequences.
title_short	A new method for inferring hidden markov models from noisy time sequences.
title_full	A new method for inferring hidden markov models from noisy time sequences.
title_fullStr	A new method for inferring hidden markov models from noisy time sequences.
title_full_unstemmed	A new method for inferring hidden markov models from noisy time sequences.
title_sort	new method for inferring hidden markov models from noisy time sequences.
publisher	Public Library of Science (PLoS)
publishDate	2012
url	https://doaj.org/article/678cd2a1d61d4313a2778f8a28d08f83
work_keys_str_mv	AT davidkelly anewmethodforinferringhiddenmarkovmodelsfromnoisytimesequences AT markdillingham anewmethodforinferringhiddenmarkovmodelsfromnoisytimesequences AT andrewhudson anewmethodforinferringhiddenmarkovmodelsfromnoisytimesequences AT karolinewiesner anewmethodforinferringhiddenmarkovmodelsfromnoisytimesequences AT davidkelly newmethodforinferringhiddenmarkovmodelsfromnoisytimesequences AT markdillingham newmethodforinferringhiddenmarkovmodelsfromnoisytimesequences AT andrewhudson newmethodforinferringhiddenmarkovmodelsfromnoisytimesequences AT karolinewiesner newmethodforinferringhiddenmarkovmodelsfromnoisytimesequences
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A new method for inferring hidden markov models from noisy time sequences.

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