Extraction and Optimization of Fuzzy Protein Sequences Classification Rules Using GRBF Neural Networks

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

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Official URL: http://bsrc.kaist.ac.kr/nip-lr/V01N01/V01N01P2-53-...


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.

Item Type: Article
Uncontrolled Keywords: Neural classification systems, data mining, rules extraction and optimization, generalized radial basis function networks, protein sequence, unimas, university, universiti, Borneo, Malaysia, Sarawak, Kuching, Samarahan, ipta, education, research, Universiti Malaysia Sarawak
Subjects: Q Science > Q Science (General)
Q Science > QA Mathematics
Divisions: Academic Faculties, Institutes and Centres > Faculty of Cognitive Sciences and Human Development
Depositing User: Karen Kornalius
Date Deposited: 12 May 2016 01:13
Last Modified: 31 May 2021 09:36
URI: http://ir.unimas.my/id/eprint/11912

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