Protein structural classification based on pseudo amino acid composition using SVM classifier

This paper deals with a structural classification by the aid of support vector machine (SVM) classifier. Amino acid composition (AAC) and pseudo amino acid composition (PseAA) features were applied with different variants. Additionally the feature reflecting the length of protein chain was taken into consideration. The SVM classifier was compared to minimal-length classifiers with respect to the AAC features. The best model of SVM classifier was chosen using grid method on the basis of cross-validation (CV) as criterion. The best model of SVM classifier is evaluated with respect to proper evaluation rates. The SCOP database and the ASTRAL tool were a source of non-homologous data to avoid the redundancy and to ensure a maximal amount of available data.