DocumentCode :
2741816
Title :
Classification of Protein Sequences using the Growing Self-Organizing Map
Author :
Ahmad, Norashikin ; Alahakoon, Damminda ; Chau, Rowena
Author_Institution :
Clayton Sch. of Inf. Technol., Monash Univ., Clayton, VIC
fYear :
2008
fDate :
12-14 Dec. 2008
Firstpage :
167
Lastpage :
172
Abstract :
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.
Keywords :
bioinformatics; data mining; pattern classification; proteins; self-organising feature maps; bioinformatics; growing self-organizing map; knowledge discovery; protein sequence classification; Artificial neural networks; Bioinformatics; Biological processes; Clustering algorithms; Databases; Dynamic programming; Heuristic algorithms; Information technology; Neural networks; Protein sequence; classification; clustering; protein sequence; self-organizing map;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Information and Automation for Sustainability, 2008. ICIAFS 2008. 4th International Conference on
Conference_Location :
Colombo
Print_ISBN :
978-1-4244-2899-1
Electronic_ISBN :
978-1-4244-2900-4
Type :
conf
DOI :
10.1109/ICIAFS.2008.4783969
Filename :
4783969
Link To Document :
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