Predict the Tertiary Structure of Protein with Error-Correcting Output Coding and Flexible Neural Tree
In this paper we intend to apply a new method to predict tertiary structure. A novel hybrid feature adopted is composed of physicochemical composition (PCC), recurrence quantification analysis (RQA) and pseudo amino acid composition (PseAA). We use the Error Correcting Output Coding (ECOC) based on three flexible neural tree models as the classifiers. 640 dataset is selected to our experiment. The predict accuracy with our method on this data set is 60.23%, higher than some other methods on the 640 datasets. So, our method is feasible and effective in some extent.
S.Z. Cai and Q.F. Zhang
Y. M. Chen and Y. H. Chen, "Predict the Tertiary Structure of Protein with Error-Correcting Output Coding and Flexible Neural Tree", Advanced Materials Research, Vols. 756-759, pp. 3781-3784, 2013