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Neural Computation
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Journal of Mathematical Psychology
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BIBE '05 Proceedings of the Fifth IEEE Symposium on Bioinformatics and Bioengineering
Neural Computation
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Neural Computation
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Computers in Biology and Medicine
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DEXA '09 Proceedings of the 2009 20th International Workshop on Database and Expert Systems Application
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FSKD '09 Proceedings of the 2009 Sixth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 07
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SOCPAR '09 Proceedings of the 2009 International Conference of Soft Computing and Pattern Recognition
A comparative study of multi-classification methods for protein fold recognition
International Journal of Computational Intelligence in Bioinformatics and Systems Biology
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ICANN/ICONIP'03 Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing
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PRIB'07 Proceedings of the 2nd IAPR international conference on Pattern recognition in bioinformatics
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IEEE Transactions on Neural Networks
A Fast and Accurate Online Sequential Learning Algorithm for Feedforward Networks
IEEE Transactions on Neural Networks
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In this paper, an intelligent hyper framework is proposed to recognize protein folds from its amino acid sequence which is a fundamental problem in bioinformatics. This framework includes some statistical and intelligent algorithms for proteins classification. The main components of the proposed framework are the Fuzzy Resource-Allocating Network (FRAN) and the Radial Bases Function based on Particle Swarm Optimization (RBF-PSO). FRAN applies a dynamic method to tune up the RBF network parameters. Due to the patterns complexity captured in protein dataset, FRAN classifies the proteins under fuzzy conditions. Also, RBF-PSO applies PSO to tune up the RBF classifier. Experimental results demonstrate that FRAN improves prediction accuracy up to 51% and achieves acceptable multi-class results for protein fold prediction. Although RBF-PSO provides reasonable results for protein fold recognition up to 48%, it is weaker than FRAN in some cases. However the proposed hyper framework provides an opportunity to use a great range of intelligent methods and can learn from previous experiences. Thus it can avoid the weakness of some intelligent methods in terms of memory, computational time and static structure. Furthermore, the performance of this system can be enhanced throughout the system life-cycle.