Clustering metagenome fragments using growing self organizing map

Overbeek, Marlinda Vasty and Kusuma, Wisnu Ananta and Buono, Agus (2013) Clustering metagenome fragments using growing self organizing map. 2013 International Conference on Advanced Computer Science and Information Systems (ICACSIS).

Full text not available from this repository.


The microorganism samples taken directly from environment are not easy to assemble because they contains mixtures of microorganism. If sample complexity is very high and comes from highly diverse environment, the difficulty of assembling DNA sequences is increasing since the interspecies chimeras can happen. To avoid this problem, in this research, we proposed binning based on composition using unsupervised learning. We employed trinucleotide and tetranucleotide frequency as features and GSOM algorithm as clustering method. GSOM was implemented to map features into high dimension feature space. We tested our method using small microbial community dataset. The quality of cluster was evaluated based on the following parameters : topographic error, quantization error, and error percentage. The evaluation results show that the best cluster can be obtained using GSOM and tetranucleotide.

Item Type: Article
Subjects: 000 Computer Science, Information and General Works > 000 Computer Science, Knowledge and Systems > 005 Computer Programming
900 History and Geography > 910 Geography and Travel
Divisions: Fakultas Teknik Informatika > Program Studi Informatika
Depositing User: mr admin umn
Date Deposited: 08 Oct 2021 16:36
Last Modified: 08 Oct 2021 16:36

Actions (login required)

View Item View Item