Propositionalisation of Profile Hidden Markov Models for biological sequence analysis

Stefan Mutter, Bernhard Pfahringer, Geoffrey Holmes

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationAI 2008
Subtitle of host publicationAdvances in Artificial Intelligence - 21st Australasian Joint Conference on Artificial Intelligence, Proceedings
Number of pages11
Publication statusPublished - 1 Dec 2008
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)0302-9743
ISSN (Electronic)1611-3349


Other21st Australasian Joint Conference on Artificial Intelligence, AI 2008
CountryNew Zealand

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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