Hidden Markov models for prediction of protein features.

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Standard

Hidden Markov models for prediction of protein features. / Bystroff, Christopher; Krogh, Anders.

In: Methods in Molecular Biology, Vol. 413, 2008, p. 173-98.

Research output: Contribution to journal › Journal article › Research › peer-review

Harvard

Bystroff, C & Krogh, A 2008, 'Hidden Markov models for prediction of protein features.', Methods in Molecular Biology, vol. 413, pp. 173-98. https://doi.org/10.1007/978-1-59745-574-9_7

APA

Bystroff, C., & Krogh, A. (2008). Hidden Markov models for prediction of protein features. Methods in Molecular Biology, 413, 173-98. https://doi.org/10.1007/978-1-59745-574-9_7

Vancouver

Bystroff C, Krogh A. Hidden Markov models for prediction of protein features. Methods in Molecular Biology. 2008;413:173-98. https://doi.org/10.1007/978-1-59745-574-9_7

Author

Bystroff, Christopher ; Krogh, Anders. / Hidden Markov models for prediction of protein features. In: Methods in Molecular Biology. 2008 ; Vol. 413. pp. 173-98.

Bibtex

@article{c2d44850dada11dcbee902004c4f4f50,
title = "Hidden Markov models for prediction of protein features.",
abstract = "Hidden Markov Models (HMMs) are an extremely versatile statistical representation that can be used to model any set of one-dimensional discrete symbol data. HMMs can model protein sequences in many ways, depending on what features of the protein are represented by the Markov states. For protein structure prediction, states have been chosen to represent either homologous sequence positions, local or secondary structure types, or transmembrane locality. The resulting models can be used to predict common ancestry, secondary or local structure, or membrane topology by applying one of the two standard algorithms for comparing a sequence to a model. In this chapter, we review those algorithms and discuss how HMMs have been constructed and refined for the purpose of protein structure prediction.",
author = "Christopher Bystroff and Anders Krogh",
note = "Key Words: Transmembrane - local - motif - Viterbi - Baum–Welch - profile - topology - folding",
year = "2008",
doi = "10.1007/978-1-59745-574-9_7",
language = "English",
volume = "413",
pages = "173--98",
journal = "Methods in Molecular Biology",
issn = "1064-3745",
publisher = "Humana Press",

}

RIS

TY - JOUR

T1 - Hidden Markov models for prediction of protein features.

AU - Bystroff, Christopher

AU - Krogh, Anders

N1 - Key Words: Transmembrane - local - motif - Viterbi - Baum–Welch - profile - topology - folding

PY - 2008

Y1 - 2008

N2 - Hidden Markov Models (HMMs) are an extremely versatile statistical representation that can be used to model any set of one-dimensional discrete symbol data. HMMs can model protein sequences in many ways, depending on what features of the protein are represented by the Markov states. For protein structure prediction, states have been chosen to represent either homologous sequence positions, local or secondary structure types, or transmembrane locality. The resulting models can be used to predict common ancestry, secondary or local structure, or membrane topology by applying one of the two standard algorithms for comparing a sequence to a model. In this chapter, we review those algorithms and discuss how HMMs have been constructed and refined for the purpose of protein structure prediction.

AB - Hidden Markov Models (HMMs) are an extremely versatile statistical representation that can be used to model any set of one-dimensional discrete symbol data. HMMs can model protein sequences in many ways, depending on what features of the protein are represented by the Markov states. For protein structure prediction, states have been chosen to represent either homologous sequence positions, local or secondary structure types, or transmembrane locality. The resulting models can be used to predict common ancestry, secondary or local structure, or membrane topology by applying one of the two standard algorithms for comparing a sequence to a model. In this chapter, we review those algorithms and discuss how HMMs have been constructed and refined for the purpose of protein structure prediction.

U2 - 10.1007/978-1-59745-574-9_7

DO - 10.1007/978-1-59745-574-9_7

M3 - Journal article

C2 - 18075166

VL - 413

SP - 173

EP - 198

JO - Methods in Molecular Biology

JF - Methods in Molecular Biology

SN - 1064-3745

ER -

ID: 2736960