Kurgan, L.A. and Homaeian, L. (2006) Prediction of Structural Classes for Protein Se-quences and Domains—Impact of Prediction Algorithms, Sequence Representation and Homology, and Test Procedures on Accuracy. Pattern Recognition, 39, 2323-2343. http://dx.doi.org/10.1016/j.patcog.2006.02.014

has been cited by the following article:

• TITLE: A Novel Prediction Method of Protein Structural Classes Based on Protein Super-Secondary Structure

  AUTHORS: Longlong Liu, Jing Cui, Jie Zhou

  KEYWORDS: Proteins, Super-Secondary Structure, Low Similarity, New Features

  JOURNAL NAME: Journal of Computer and Communications, Vol.4 No.15, November 28, 2016

  ABSTRACT: At present, the feature extraction of protein sequences is the most basic issue to predict protein structural classes and is also the key problem to decide the quality of prediction. In order to predict protein structural classes accurately, we construct a 14-dimensional feature vector based on protein secondary and super-secondary structure information to reflect the content and spatial ordering of the given protein sequences. Among the vector, seven features about -helix bundle, hairpin motifs, Rossman folds, -plaits and other super-secondary structure information are first proposed in our paper. Experiments show that our method improves overall accuracy of lower similarity datasets 1189 and 640 by 0.9% - 3.8% and 0.5% - 4.2% respectively compared with other methods and has a competitive advantage for predicting proteins in and classes.