@article {10.3844/ajbbsp.2018.67.75,
article_type = {journal},
title = {Low-Homology Protein Structural Class Prediction from Secondary Structure Based on Visibility and Horizontal Visibility Network},
author = {Zhao, Zhi-Qin and Luo, Liang and Liu, Xiao-Yan},
volume = {14},
number = {1},
year = {2018},
month = {Mar},
pages = {67-75},
doi = {10.3844/ajbbsp.2018.67.75},
url = {https://thescipub.com/abstract/ajbbsp.2018.67.75},
abstract = {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.},
journal = {American Journal of Biochemistry and Biotechnology},
publisher = {Science Publications}
}