Extraction and Optimization of Fuzzy Protein Sequences Classification Rules Using GRBF Neural Networks
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. Neura...
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my.unimas.ir.119122021-05-31T09:36:48Z http://ir.unimas.my/id/eprint/11912/ Extraction and Optimization of Fuzzy Protein Sequences Classification Rules Using GRBF Neural Networks Wang, Dianhui Lee, Nung Kion Dillon, Tharam S. Q Science (General) QA Mathematics 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 about 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 be 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. Neural Information Processing Systems ( NIPS ) 2003 Article PeerReviewed text en http://ir.unimas.my/id/eprint/11912/7/wang.pdf Wang, Dianhui and Lee, Nung Kion and Dillon, Tharam S. (2003) Extraction and Optimization of Fuzzy Protein Sequences Classification Rules Using GRBF Neural Networks. Neural Information Processing-Letters and Reviews, 1 (1). pp. 53-59. ISSN 1738-2572 http://bsrc.kaist.ac.kr/nip-lr/V01N01/V01N01P2-53-59.pdf |
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Q Science (General) QA Mathematics Wang, Dianhui Lee, Nung Kion Dillon, Tharam S. Extraction and Optimization of Fuzzy Protein Sequences Classification Rules Using GRBF Neural Networks |
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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 about 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 be 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. |
format |
Article |
author |
Wang, Dianhui Lee, Nung Kion Dillon, Tharam S. |
author_facet |
Wang, Dianhui Lee, Nung Kion Dillon, Tharam S. |
author_sort |
Wang, Dianhui |
title |
Extraction and Optimization of Fuzzy Protein Sequences Classification Rules Using GRBF Neural Networks |
title_short |
Extraction and Optimization of Fuzzy Protein Sequences Classification Rules Using GRBF Neural Networks |
title_full |
Extraction and Optimization of Fuzzy Protein Sequences Classification Rules Using GRBF Neural Networks |
title_fullStr |
Extraction and Optimization of Fuzzy Protein Sequences Classification Rules Using GRBF Neural Networks |
title_full_unstemmed |
Extraction and Optimization of Fuzzy Protein Sequences Classification Rules Using GRBF Neural Networks |
title_sort |
extraction and optimization of fuzzy protein sequences classification rules using grbf neural networks |
publisher |
Neural Information Processing Systems ( NIPS ) |
publishDate |
2003 |
url |
http://ir.unimas.my/id/eprint/11912/7/wang.pdf http://ir.unimas.my/id/eprint/11912/ http://bsrc.kaist.ac.kr/nip-lr/V01N01/V01N01P2-53-59.pdf |
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13.19449 |