Propositionalisation of Profile Hidden Markov Models for Biological Sequence Analysis

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.