Dipeptide based SVM model for prediction of CDKs and cyclins

Various combination of both cyclin dependent kinases (CDKs) and cyclin proteins are responsible for progression of cell cycle through various phases like G1, S, G2 and M. CDKs are enzymes with possible role to play in anti cancer therapy. Realizing the importance of both these proteins in various aspects of life a new efficient computational model has been developed using parameters like dipeptide composition for prediction of these proteins. The support vector machine (SVM) package used has been implemented using freely downloadable software LIBsvm. With five fold cross validation accuracy of 99.9644% has been achieved in predicting the two classes using dipeptide composition (DPC). Further the accuracy of test module came out to be 95.6989%.