Protein folding, prediction of protein structure and functions are most important problems in bioinformatics.
The protein fold process mainly reflects in the kinetic order of folding. Predicting the structural classes of low-homology
protein is a difficult problem in the prediction of protein structure. In order to understanding the mechanism of programmed
cell death, it is very necessary to obtain the information about subcellular locations and functions of apoptosis
proteins. Predicting protein subnuclear localizations is a challenging problem which is harder than predicting protein subcellular
locations. Predicting membrane protein types is related to the structure and function of proteins. In this review, we
introduce some applications of nonlinear science methods and support vector machine methods to the above protein problems.
The nonlinear science methods including the horizontal visibility network, kernel method, recurrence quantification
analysis, global descriptor, Lempel-Ziv complexity, and Hilbert-Huang transform are used to extract features in these approaches.