Propositionalisation of multiple sequence alignments using probabilistic models

Stefan Mutter, Bernhard Pfahringer, Geoff Holmes

Research output: Contribution to conferencePaperpeer-review


Multiple sequence alignments play a central role in Bioin-formatics. Most alignment representations are designed to facilitate knowledge extraction by human experts. Additionally statistical models like Profile Hidden Markov Models are used as representations. They offer the advantage to provide sound, probabilistic scores. The basic idea we present in this paper is to use the structure of a Profile Hidden Markov Model for propositionalisation. This way we get a simple, extendable representation of multiple sequence alignments which facilitates further analysis by Machine Learning algorithms.

Original languageEnglish
Number of pages4
Publication statusPublished or Issued - 2008
Externally publishedYes
Event6th New Zealand Computer Science Research Student Conference, NZCSRSC 2008 - Christchurch, New Zealand
Duration: 14 Apr 200818 Apr 2008


Other6th New Zealand Computer Science Research Student Conference, NZCSRSC 2008
Country/TerritoryNew Zealand


  • Hidden Markov
  • Model
  • Multiple sequence alignment
  • Propositionalisation
  • Representation

ASJC Scopus subject areas

  • Computer Science (miscellaneous)
  • Education

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