Propositionalisation of Profile Hidden Markov Models for Biological Sequence Analysis

Authors:
Stefan Mutter;Bernhard Pfahringer;Geoffrey Holmes
Affiliations:
Department of Computer Science, The University of Waikato, Hamilton, New Zealand;Department of Computer Science, The University of Waikato, Hamilton, New Zealand;Department of Computer Science, The University of Waikato, Hamilton, New Zealand
Venue:
AI '08 Proceedings of the 21st Australasian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
Year:
2008

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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.