Classification of Protein Sequences using the Growing Self-Organizing Map
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 identif...
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| Format: | Conference or Workshop Item |
| Language: | English |
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2008
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| Online Access: | http://eprints.utem.edu.my/id/eprint/90/ http://eprints.utem.edu.my/id/eprint/90/1/Norashikin__iciafs2008.pdf |
| _version_ | 1848886884861739008 |
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| author | Ahmad, N. |
| author_facet | Ahmad, N. |
| author_sort | Ahmad, N. |
| building | UTeM Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-15T19:45:35Z |
| format | Conference or Workshop Item |
| id | utem-90 |
| institution | Universiti Teknikal Malaysia Melaka |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T19:45:35Z |
| publishDate | 2008 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | utem-902015-05-28T02:16:42Z http://eprints.utem.edu.my/id/eprint/90/ Classification of Protein Sequences using the Growing Self-Organizing Map Ahmad, N. Q Science (General) 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. 2008-12 Conference or Workshop Item NonPeerReviewed application/pdf en http://eprints.utem.edu.my/id/eprint/90/1/Norashikin__iciafs2008.pdf 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. . |
| spellingShingle | Q Science (General) Ahmad, N. Classification of Protein Sequences using the Growing Self-Organizing Map |
| title | Classification of Protein Sequences using the Growing Self-Organizing Map |
| title_full | Classification of Protein Sequences using the Growing Self-Organizing Map |
| title_fullStr | Classification of Protein Sequences using the Growing Self-Organizing Map |
| title_full_unstemmed | Classification of Protein Sequences using the Growing Self-Organizing Map |
| title_short | Classification of Protein Sequences using the Growing Self-Organizing Map |
| title_sort | classification of protein sequences using the growing self-organizing map |
| topic | Q Science (General) |
| url | http://eprints.utem.edu.my/id/eprint/90/ http://eprints.utem.edu.my/id/eprint/90/1/Norashikin__iciafs2008.pdf |