FRAN and RBF-PSO as two components of a hyper framework to recognize protein folds |
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| Institution: | 1. Department of Computer Science, Amirkabir University of Technology, N. 424, Hafez Ave, Tehran, Iran;2. Laboratory of Network Optimization Research Center (NORC), Faculty of Mathematics and Computer Science, Amirkabir University of Technology, N. 424, Hafez Ave, Tehran, Iran;1. Dipartimento di Ingegneria dell’Informazione (DINFO) - Università di Firenze, 50139 Firenze, Italy;2. ITM, Faculty of Mathematics and Natural Sciences, University of Groningen, 9747 AG Groningen, The Netherlands;1. School of Electrical Engineering and Computer Science, University of Newcastle, New South Wales 2308, Australia;2. Donhad Pty Ltd., Newcastle, Australia;1. Department of Radiology, A.U.O. Cagliari, Cagliari, Italy;2. Biolab, Department of Electronics and Telecommunications, Politecnico di Torino, Torino, Italy;3. Department of ECE, Ngee Ann Polytechnic, Singapore, Singapore;4. Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Malaysia;5. Vascular Diagnostic Center, Nicosia, Cyprus;6. Fellow AIMBE, CTO, Diagnostic and Monitoring Division, AtheroPoint LLC, CA, USA;7. Biomedical Engineering Division, Global Biomedical Technologies Inc., CA, USA;8. Biomedical Engineering Department, University of Idaho (Aff.), ID, USA;1. Systems and Control Laboratory, Computer and Automation Research Institute of Hungarian Academy of Sciences, P.O. Box 63, H-1518 Budapest, Hungary;2. Research and Development Institute of Knorr-Bremse Brake-Systems Gmbh., Major u. 69, H-1119 Budapest, Hungary |
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| Abstract: | 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. |
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| Keywords: | Protein Fold Recognition Machine learning Instance-based method MLP RBF PSO |
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