FRAN and RBF-PSO as two components of a hyper framework to recognize protein folds.
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.
Department of Computer Science, Amirkabir University of Technology, N. 424, Hafez Ave, Tehran, Iran.
Elham
E
Abbasi
Mehdi
M
Ghatee
M E
ME
Shiri
D030562:Databases, Protein; D017510:Protein Folding; D011506:Proteins / Q000737:chemistry / Q000235:genetics*; D020539:Sequence Analysis, Protein / Q000295:instrumentation / Q000379:methods*; D012984:Software
D011506:Proteins
Instance-based method; MLP; Machine learning; PSO; Protein Fold Recognition; RBF