Proceedings of the Korean Society for Bioinformatics Conference (한국생물정보학회:학술대회논문집)
- 2005.09a
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- Pages.124-129
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- 2005
Region Identification on a Trained Growing Self-Organizing Map for Sequence Separation between Different Phylogenetic Genomes
- Reinhard, Johannes (Department of Computer Science, University of Magdeburg) ;
- Chan, Chon-Kit Kenneth (Mechatronics Research Group, Department of Mechanical and Manufacturing Engineering, The University of Melbourne) ;
- Halgamuge, Saman K. (Mechatronics Research Group, Department of Mechanical and Manufacturing Engineering, The University of Melbourne) ;
- Tang, Sen-Lin (School of Veterinary Science, The University of Melbourne) ;
- Kruse, Rudolf (Department of Computer Science, University of Magdeburg)
- Published : 2005.09.22
Abstract
The Growing Self-Organizing Map (GSOM), an extended type of the Self-Organizing Map, is a widely accepted tool for clustering high dimensional data. It is also suitable for the clustering of short DNA sequences of phylogenetic genomes by their oligonucleotide frequency. The GSOM presents the result of the clustering process visually on a coloured map, where the clusters can be identified by the user. This paper describes a proposal for automatic cluster detection on this map without any participation by the user. It has been applied with good success on 20 different data sets for the purpose of species separation.
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