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Algorithms for Hidden Markov Models Restricted to Occurrences of Regular Expressions

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Algorithms for Hidden Markov Models Restricted to Occurrences of Regular Expressions. / Tataru, Paula; Sand, Andreas; Hobolth, Asger; Mailund, Thomas; Pedersen, Christian Nørgaard Storm.

I: Biology, Bind 2, Nr. 4, 08.11.2013, s. 1282-1295.

Publikation: Bidrag til tidsskrift/Konferencebidrag i tidsskrift /Bidrag til avisTidsskriftartikelForskningpeer review

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@article{ad9e01e90288478587dc9ad106c653f0,
title = "Algorithms for Hidden Markov Models Restricted to Occurrences of Regular Expressions",
abstract = "Hidden Markov Models (HMMs) are widely used probabilistic models, particularly for annotating sequential data with an underlying hidden structure. Patterns in the annotation are often more relevant to study than the hidden structure itself. A typical HMM analysis consists of annotating the observed data using a decoding algorithm and analyzing the annotation to study patterns of interest. For example, given an HMM modeling genes in DNA sequences, the focus is on occurrences of genes in the annotation. In this paper, we define a pattern through a regular expression and present a restriction of three classical algorithms to take the number of occurrences of the pattern in the hidden sequence into account. We present a new algorithm to compute the distribution of the number of pattern occurrences, and we extend the two most widely used existing decoding algorithms to employ information from this distribution. We show experimentally that the expectation of the distribution of the number of pattern occurrences gives a highly accurate estimate, while the typical procedure can be biased in the sense that the identified number of pattern occurrences does not correspond to the true number. We furthermore show that using this distribution in the decoding algorithms improves the predictive power of the model.",
author = "Paula Tataru and Andreas Sand and Asger Hobolth and Thomas Mailund and Pedersen, {Christian N{\o}rgaard Storm}",
year = "2013",
month = nov,
day = "8",
doi = "10.3390/biology2041282",
language = "English",
volume = "2",
pages = "1282--1295",
journal = "Biology",
issn = "2079-7737",
publisher = "MDPI AG",
number = "4",

}

RIS

TY - JOUR

T1 - Algorithms for Hidden Markov Models Restricted to Occurrences of Regular Expressions

AU - Tataru, Paula

AU - Sand, Andreas

AU - Hobolth, Asger

AU - Mailund, Thomas

AU - Pedersen, Christian Nørgaard Storm

PY - 2013/11/8

Y1 - 2013/11/8

N2 - Hidden Markov Models (HMMs) are widely used probabilistic models, particularly for annotating sequential data with an underlying hidden structure. Patterns in the annotation are often more relevant to study than the hidden structure itself. A typical HMM analysis consists of annotating the observed data using a decoding algorithm and analyzing the annotation to study patterns of interest. For example, given an HMM modeling genes in DNA sequences, the focus is on occurrences of genes in the annotation. In this paper, we define a pattern through a regular expression and present a restriction of three classical algorithms to take the number of occurrences of the pattern in the hidden sequence into account. We present a new algorithm to compute the distribution of the number of pattern occurrences, and we extend the two most widely used existing decoding algorithms to employ information from this distribution. We show experimentally that the expectation of the distribution of the number of pattern occurrences gives a highly accurate estimate, while the typical procedure can be biased in the sense that the identified number of pattern occurrences does not correspond to the true number. We furthermore show that using this distribution in the decoding algorithms improves the predictive power of the model.

AB - Hidden Markov Models (HMMs) are widely used probabilistic models, particularly for annotating sequential data with an underlying hidden structure. Patterns in the annotation are often more relevant to study than the hidden structure itself. A typical HMM analysis consists of annotating the observed data using a decoding algorithm and analyzing the annotation to study patterns of interest. For example, given an HMM modeling genes in DNA sequences, the focus is on occurrences of genes in the annotation. In this paper, we define a pattern through a regular expression and present a restriction of three classical algorithms to take the number of occurrences of the pattern in the hidden sequence into account. We present a new algorithm to compute the distribution of the number of pattern occurrences, and we extend the two most widely used existing decoding algorithms to employ information from this distribution. We show experimentally that the expectation of the distribution of the number of pattern occurrences gives a highly accurate estimate, while the typical procedure can be biased in the sense that the identified number of pattern occurrences does not correspond to the true number. We furthermore show that using this distribution in the decoding algorithms improves the predictive power of the model.

U2 - 10.3390/biology2041282

DO - 10.3390/biology2041282

M3 - Journal article

C2 - 24833225

VL - 2

SP - 1282

EP - 1295

JO - Biology

JF - Biology

SN - 2079-7737

IS - 4

ER -