Propositionalisation of Profile Hidden Markov Models for biological sequence analysis

Stefan Mutter, Bernhard Pfahringer, Geoffrey Holmes

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

Hidden Markov Models are a widely used generative model for analysing sequence data. A variant, Profile Hidden Markov Models are a special case used in Bioinformatics to represent, for example, protein families. In this paper we introduce a simple propositionalisation method for Profile Hidden Markov Models. The method allows the use of PHMMs discriminatively in a classification task. Previously, kernel approaches have been proposed to generate a discriminative description for an HMM, but require the explicit definition of a similarity measure for HMMs. Propositionalisation does not need such a measure and allows the use of any propositional learner including kernel-based approaches. We show empirically that using propositionalisation leads to higher accuracies in comparison with PHMMs on benchmark datasets.

LanguageEnglish
Title of host publicationAI 2008: Advances in Artificial Intelligence - 21st Australasian Joint Conference on Artificial Intelligence, Proceedings
Pages278-288
Number of pages11
Volume5360 LNAI
DOIs
Publication statusPublished - 2008
Externally publishedYes
Event21st Australasian Joint Conference on Artificial Intelligence, AI 2008 - Auckland, New Zealand
Duration: 1 Dec 20085 Dec 2008

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5360 LNAI
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other21st Australasian Joint Conference on Artificial Intelligence, AI 2008
CountryNew Zealand
CityAuckland
Period1/12/085/12/08

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Mutter, S., Pfahringer, B., & Holmes, G. (2008). Propositionalisation of Profile Hidden Markov Models for biological sequence analysis. In AI 2008: Advances in Artificial Intelligence - 21st Australasian Joint Conference on Artificial Intelligence, Proceedings (Vol. 5360 LNAI, pp. 278-288). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5360 LNAI). https://doi.org/10.1007/978-3-540-89378-3_27
Mutter, Stefan ; Pfahringer, Bernhard ; Holmes, Geoffrey. / Propositionalisation of Profile Hidden Markov Models for biological sequence analysis. AI 2008: Advances in Artificial Intelligence - 21st Australasian Joint Conference on Artificial Intelligence, Proceedings. Vol. 5360 LNAI 2008. pp. 278-288 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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Mutter, S, Pfahringer, B & Holmes, G 2008, Propositionalisation of Profile Hidden Markov Models for biological sequence analysis. in AI 2008: Advances in Artificial Intelligence - 21st Australasian Joint Conference on Artificial Intelligence, Proceedings. vol. 5360 LNAI, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 5360 LNAI, pp. 278-288, 21st Australasian Joint Conference on Artificial Intelligence, AI 2008, Auckland, New Zealand, 1/12/08. https://doi.org/10.1007/978-3-540-89378-3_27

Propositionalisation of Profile Hidden Markov Models for biological sequence analysis. / Mutter, Stefan; Pfahringer, Bernhard; Holmes, Geoffrey.

AI 2008: Advances in Artificial Intelligence - 21st Australasian Joint Conference on Artificial Intelligence, Proceedings. Vol. 5360 LNAI 2008. p. 278-288 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5360 LNAI).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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AB - Hidden Markov Models are a widely used generative model for analysing sequence data. A variant, Profile Hidden Markov Models are a special case used in Bioinformatics to represent, for example, protein families. In this paper we introduce a simple propositionalisation method for Profile Hidden Markov Models. The method allows the use of PHMMs discriminatively in a classification task. Previously, kernel approaches have been proposed to generate a discriminative description for an HMM, but require the explicit definition of a similarity measure for HMMs. Propositionalisation does not need such a measure and allows the use of any propositional learner including kernel-based approaches. We show empirically that using propositionalisation leads to higher accuracies in comparison with PHMMs on benchmark datasets.

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Mutter S, Pfahringer B, Holmes G. Propositionalisation of Profile Hidden Markov Models for biological sequence analysis. In AI 2008: Advances in Artificial Intelligence - 21st Australasian Joint Conference on Artificial Intelligence, Proceedings. Vol. 5360 LNAI. 2008. p. 278-288. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-540-89378-3_27