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dc.contributor.authorMutter, Stefan
dc.contributor.authorPfahringer, Bernhard
dc.contributor.authorHolmes, Geoffrey
dc.coverage.spatialConference held at Christchurch, New Zealanden_NZ
dc.date.accessioned2013-10-23T04:12:19Z
dc.date.available2013-10-23T04:12:19Z
dc.date.issued2008
dc.identifier.citationMutter, S., Pfahringer, B., & Holmes, G. (2008). Propositionalisation of multiple sequence alignments using probabilistic models. In Proceedings of the New Zealand Computer Science Research Student Conference 2008 (pp. 234-237).en_NZ
dc.identifier.urihttps://hdl.handle.net/10289/8107
dc.description.abstractMultiple sequence alignments play a central role in Bioinformatics. 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 algorighms.en_NZ
dc.format.mimetypeapplication/pdf
dc.language.isoenen_NZ
dc.publisherCanterbury Universityen_NZ
dc.rights© 2008 the authors.en_NZ
dc.subjectcomputer scienceen_NZ
dc.subjectmultiple sequence alignment representationen_NZ
dc.subjectHidden Markov Modelen_NZ
dc.subjectpropositionalisationen_NZ
dc.subjectMachine learning
dc.titlePropositionalisation of multiple sequence alignments using probabilistic modelsen_NZ
dc.typeConference Contributionen_NZ
dc.relation.isPartOfNew Zealand Computer Science Research Student Conference (NZCSRSC 2008)en_NZ
pubs.begin-page234en_NZ
pubs.elements-id17689
pubs.end-page237en_NZ
pubs.finish-date2008-04-18en_NZ
pubs.start-date2008-04-14en_NZ


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