TY  - JOUR
AU  - Zhao, Zhi-Qin 
AU  - Luo, Liang 
AU  - Liu, Xiao-Yan 
PY  - 2018
TI  - Low-Homology Protein Structural Class Prediction from Secondary Structure Based on Visibility and Horizontal Visibility Network
JF  - American Journal of Biochemistry and Biotechnology
VL  - 14
IS  - 1
DO  - 10.3844/ajbbsp.2018.67.75
UR  - https://thescipub.com/abstract/ajbbsp.2018.67.75
AB  - In this study, based on the predicted secondary structures of proteins, we propose a new approach to predict protein structural classes (α,β,α/β,α+β) for three widely used low-homology data sets. Fist, we obtain two time siries from the chaos game representation of each predicted secondary structure; second, based on two time series, we construct visibility and horizontal visibility network, respectively and generate a set of features using 17 network features; finaly, we predict each protein structure class using support vector machine and Fisher’s linear discriminant algorithm, respectively. In order to evaluate our method, the leave one out cross-validating test is employed on three data sets. Results show that our approach has been provided as a effective tool for the prediction of low-homology protein structural classes.