Improved Protein Structural Class Prediction Based on Chaos Game Representation

Determination of protein structural class from sequence information is a challenging task. In this paper, at first we apply chaos game representation to protein sequences and extract two time series then using phase space reconstruction theory and calculate phase space of all time series. Next, applying recurrence quantification analysis (RQA). For each protein sequence 16 characteristic parameters can be calculated with RQA. In order to classification we propose an ensemble classification method. The 10 fold cross validation test is used to test and compare our method with other existing methods. The overall accuracy for two datasets 1189 and 25PDB are 66.7% and 68.2% respectively that has much better performance toward compared methods.