Abstract
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 language | English |
|---|---|
| Pages | 234-237 |
| Number of pages | 4 |
| Publication status | Published or Issued - 2008 |
| Externally published | Yes |
| Event | 6th New Zealand Computer Science Research Student Conference, NZCSRSC 2008 - Christchurch, New Zealand Duration: 14 Apr 2008 → 18 Apr 2008 |
Other
| Other | 6th New Zealand Computer Science Research Student Conference, NZCSRSC 2008 |
|---|---|
| Country/Territory | New Zealand |
| City | Christchurch |
| Period | 14/04/08 → 18/04/08 |
Keywords
- Hidden Markov
- Model
- Multiple sequence alignment
- Propositionalisation
- Representation
ASJC Scopus subject areas
- Computer Science (miscellaneous)
- Education