Protein Structural Prediction Based on Flexible Neural Tree

This paper proposes a method of protein structural prediction classes based on flexible neural tree.The approximate entropy and hydrophobicity pattern of a protein sequence are used to characterize the Pseudo-Amino Acid(PseAA) components.It extracts features of protein in data set.For a given protein sequence sample,a 27-D PseAA composition is generated as its descriptor.PseAA composition features as input data,the flexible neural tree is adopted as the prediction engine.A classification method named M-ary classifier is introduced.The 640 protein sequence is used as the dataset.Experimental result shows the method has better optimization of performance and improves the predictive accuracy rate.