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Propositionalisation of multiple sequence alignments using probabilistic models

Abstract
Multiple 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.
Type
Conference Contribution
Type of thesis
Series
Citation
Mutter, 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).
Date
2008
Publisher
Canterbury University
Degree
Supervisors
Rights
© 2008 the authors.