DocumentCode :
1643591
Title :
An improved Hybrid Neuro Fuzzy Genetic System (I-HNFGS) for protein secondary structure prediction from amino acid sequence
Author :
Krishnaji, Andey ; Rao, A. Ananda
Author_Institution :
Dept. of Comput. Applic., Swarnandhra Coll. of Eng. & Technol., Narasapur, India
fYear :
2013
Firstpage :
1218
Lastpage :
1223
Abstract :
This paper proposes few improvements to “Hybrid Neuro Fuzzy Genetic System (HNFGS)”, which we have implemented and presented in [13] for protein secondary structure prediction. The hybridization of artificial neural networks, genetic algorithms, and fuzzy logic can produce robust solutions for complex prediction problems, such as protein secondary structure prediction. Due to the complex and dynamic nature of biological data, we have proposed a two step process to model protein secondary structure prediction. As the protein secondary structure prediction problem involves a huge number of inputs, the input variables of I-HNGS are selected carefully in the first phase. In the second phase genetic algorithms are used to optimize fuzzy set definition and the shape and type of fuzzy membership functions. The improved HNFGS has produced better prediction results when experimented on three-class (alpha-helix, beta-sheet or coil) protein secondary structure prediction from amino acid sequence. The experimental results indicate that the proposed system has the advantages of high precision, good generalization, and comprehensibility. This system also exhibits the property of rapid convergence in fuzzy rule generation.
Keywords :
biology computing; fuzzy logic; fuzzy neural nets; genetic algorithms; molecular biophysics; proteins; I-HNFGS; alpha-helix protein secondary structure; amino acid sequence; artificial neural networks; beta-sheet protein secondary structure; biological data; coil protein secondary structure; comprehensibility property; fuzzy logic; fuzzy membership functions; fuzzy rule generation; generalization property; genetic algorithms; high precision property; improved hybrid neuro fuzzy genetic system; protein secondary structure prediction; rapid convergence property; Decision support systems; Heuristic algorithms; Prediction algorithms; Proteins; Shape; Sociology; Statistics; Artificial Neural Networks; Fuzzy Logic; Genetic Algorithms; Hybrid Neuro Fuzzy Genetic System (HNFGS); Protein Secondary Structure;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Advances in Computing, Communications and Informatics (ICACCI), 2013 International Conference on
Conference_Location :
Mysore
Print_ISBN :
978-1-4799-2432-5
Type :
conf
DOI :
10.1109/ICACCI.2013.6637351
Filename :
6637351
Link To Document :
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