Classification of Protein Homo-oligomers Using Support Vector Machine

The homo-dimer, homo-trimer, homo-tetramer and homo-hexamer of protein were classified using both of support vector machine and Bayes covariant discriminant methods. It was found that the total accuracies of "one-versus-rest" and "all-versus-all" are 77.36% and 93. 43% respectively using support vector machine in jackknife test, which are 26. 72 and 42. 79 percentile higher respectively than that of Bayes covariant discriminant method in the same test. These results show that the support vector machine is a specially effective method for classifying the higher protein homo-oligomers from protein primary sequences. Using "all-versus-all" policy is better than "one-versus-rest" policy for classifying homo-oligomers based on the same machine learning method (such as support vector machine). And it was also indicated that the primary sequences of homo-oligomeric proteins contain quaternary information.