Data Mining for Building Neural Protein Sequence Classification Systems with Improved Performance
Traditionally, two protein sequences are classified into the same class if their feature patterns have high homology. These feature patterns were originally extracted by sequence alignment algorithms, which measure similarity between an unseen protein sequence and identified protein sequences. Neur...
| Main Authors: | , , |
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| Format: | Proceeding |
| Language: | English |
| Published: |
IEEE
2003
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| Subjects: | |
| Online Access: | http://ir.unimas.my/id/eprint/11927/ http://ir.unimas.my/id/eprint/11927/1/Data%20Mining_abstract.pdf |
| _version_ | 1848837090662416384 |
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| author | Wang, Dianhui Lee, Nung Kion Dillon, Tharam S. |
| author_facet | Wang, Dianhui Lee, Nung Kion Dillon, Tharam S. |
| author_sort | Wang, Dianhui |
| building | UNIMAS Institutional Repository |
| collection | Online Access |
| description | Traditionally, two protein sequences are classified
into the same class if their feature patterns have high homology. These feature patterns were originally extracted by sequence alignment algorithms, which measure similarity between an unseen protein sequence and identified protein sequences. Neural network approaches, while reasonably accurate at classification, give no information ahout the relationship between the unseen case and the classified items that is useful to biologist. In contrast, in this paper we use a generalized radial basis function (GRBF)
neural network architecture 'that generates fuzzy classification rules that could he used for further knowledge discovery. Our proposed techniques were evaluated using protein sequences with ten classes of super-families downloaded from a public domain database, and the results compared favorably with other standard machine learning techniques. |
| first_indexed | 2025-11-15T06:34:08Z |
| format | Proceeding |
| id | unimas-11927 |
| institution | Universiti Malaysia Sarawak |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T06:34:08Z |
| publishDate | 2003 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | unimas-119272016-05-12T04:32:07Z http://ir.unimas.my/id/eprint/11927/ Data Mining for Building Neural Protein Sequence Classification Systems with Improved Performance Wang, Dianhui Lee, Nung Kion Dillon, Tharam S. QA75 Electronic computers. Computer science T Technology (General) Traditionally, two protein sequences are classified into the same class if their feature patterns have high homology. These feature patterns were originally extracted by sequence alignment algorithms, which measure similarity between an unseen protein sequence and identified protein sequences. Neural network approaches, while reasonably accurate at classification, give no information ahout the relationship between the unseen case and the classified items that is useful to biologist. In contrast, in this paper we use a generalized radial basis function (GRBF) neural network architecture 'that generates fuzzy classification rules that could he used for further knowledge discovery. Our proposed techniques were evaluated using protein sequences with ten classes of super-families downloaded from a public domain database, and the results compared favorably with other standard machine learning techniques. IEEE 2003 Proceeding NonPeerReviewed text en http://ir.unimas.my/id/eprint/11927/1/Data%20Mining_abstract.pdf Wang, Dianhui and Lee, Nung Kion and Dillon, Tharam S. (2003) Data Mining for Building Neural Protein Sequence Classification Systems with Improved Performance. In: Neural Networks, 2003. Proceedings of the International Joint Conference on, 20-24 July 2003. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1223671 10.1109/IJCNN.2003.1223671 |
| spellingShingle | QA75 Electronic computers. Computer science T Technology (General) Wang, Dianhui Lee, Nung Kion Dillon, Tharam S. Data Mining for Building Neural Protein Sequence Classification Systems with Improved Performance |
| title | Data Mining for Building Neural Protein Sequence Classification Systems with Improved Performance |
| title_full | Data Mining for Building Neural Protein Sequence Classification Systems with Improved Performance |
| title_fullStr | Data Mining for Building Neural Protein Sequence Classification Systems with Improved Performance |
| title_full_unstemmed | Data Mining for Building Neural Protein Sequence Classification Systems with Improved Performance |
| title_short | Data Mining for Building Neural Protein Sequence Classification Systems with Improved Performance |
| title_sort | data mining for building neural protein sequence classification systems with improved performance |
| topic | QA75 Electronic computers. Computer science T Technology (General) |
| url | http://ir.unimas.my/id/eprint/11927/ http://ir.unimas.my/id/eprint/11927/ http://ir.unimas.my/id/eprint/11927/ http://ir.unimas.my/id/eprint/11927/1/Data%20Mining_abstract.pdf |