Protein fold recognition using Deep Kernelized Extreme Learning Machine and linear discriminant analysis

Protein fold recognition is considered as an essential step in determining the tertiary structure of proteins in bioinformatics. The most complex challenge in the protein folding problem is the high dimensionality of feature vectors and the diversity of the protein fold classes. In this paper, two frameworks are proposed to solve this problem. The main components in the first two-level framework are Deep Kernelized Extreme Learning Machine (DKELM) and linear discriminant analysis. Second framework consists of three levels. In the first level, the dataset is initialized to be used in the next level. In the second level, OVADKELM and OVODKELM are independently employed to extract four and six new features, respectively, which are added into the basic datasets in the third level. DKELM is applied in the third level as a final classifier to classify the instances into folds. The proposed frameworks are implemented on DD and TG datasets which are considered as SCOP datasets. The experimental results indicate that proposed methods could improve the classification accuracy in both datasets.