Implementation of a hybrid Neuro Fuzzy Genetic System for improving protein secondary structure prediction

Abstract

The hybridization and application of computational intelligence techniques such as artificial neural networks, fuzzy logic, and genetic algorithms has become hotspot research area in the recent times. This paper presents a work which investigates the benefits of combining genetic algorithms, fuzzy logic and artificial neural networks into a hybrid Neuro Fuzzy Genetic System, especially for the prediction of protein secondary structure. The proposed Neuro Fuzzy Genetic System will assign a secondary structure type for each residue in the target protein sequence by way of including more biological information such as protein structural class, solvent accessibility, and physicochemical properties of residues in order to improve accuracy of protein secondary structure prediction. The proposed system will experiment on three-class secondary structure prediction of proteins, that is, alpha helix, beta sheet or coil. The experimental results indicate that the proposed method has the advantages of high precision, good generalization, and comprehensibility. The method also exhibits the property of rapid convergence in fuzzy rule generation.