Classification of Protein Sequences using the Growing Self-Organizing Map

Ahmad, N. (2008) Classification of Protein Sequences using the Growing Self-Organizing Map. In: 4th International Conference on Information and Automation for Sustainability, 2008. ICIAFS 2008. .

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Protein sequence analysis is an important task in bioinformatics. The classification of protein sequences into groups is beneficial for further analysis of the structures and roles of a particular group of protein in biological process. It also allows an unknown or newly found sequence to be identified by comparing it with protein groups that have already been studied. In this paper, we present the use of growing self-organizing map (GSOM), an extended version of the self-organizing map (SOM) in classifying protein sequences. With its dynamic structure, GSOM facilitates the discovery of knowledge in a more natural way. This study focuses on two aspects; analysis of the effect of spread factor parameter in the GSOM to the node growth and the identification of grouping and subgrouping under different level of abstractions by using the spread factor.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: bioinformatics;growing self-organizing map;knowledge discovery;protein sequence classification;bioinformatics;data mining;pattern classification;proteins;self-organising feature maps;
Subjects: Q Science > Q Science (General)
Divisions: Faculty of Information and Communication Technology > Department of Software Engineeering
Depositing User: Dr. Norashikin Ahmad
Date Deposited: 08 Aug 2011 04:09
Last Modified: 28 May 2015 02:16
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