Classifying ordered-disordered proteins using linear and kernel support vector machines
AbstractIntroduction:Intrinsically disordered proteins occur when the deformations happen in the tertiary structure of a protein. Disordered proteins play an important role in DNA/RNA/protein recognition, modulation of specificity/affinity of protein binding, molecular threading, activation by cleavage. The aim of the study is the identification of ordered-disordered protein which is a very challenging problem in bioinformatics.Methods:In this paper, this kind of proteins is classified by using linear and kernel (nonlinear) support vector machines (SVM).Results:Overall accuracy rate of linear SVM and kernel SVM in identifying the ordered-disordered proteins are 86.54% and 94.23%, respectively.Discussion and conclusion:Since kernel SVM gives the best discriminating scheme, it can be referred that it is a very satisfying method to identify ordered-disordered structures of proteins.