A Novel Protein Structural Classes Prediction Method Based on Hierarchical Classification Model

摘要

In the post-genomic era prediction of protein structural classes is an important area in bioinformatics, it is beneficial to research protein function, regulation and interactions. In this paper, a novel hierarchical classification model based on flexible neural tree (FNT) was been built, different features were extracted based on the predicted secondary structure sequence and the corresponding E-H sequence for every classifiers. Three datasets with low homology were used to test the proposed method compared to existing methods. The overall accuracy of this method is all improved on three datasets.