Support Vector Machine Approach for Isomerases Prediction Problem

As the proteinic enzyme sequences are entering the databases at a prodigious rate, the functional annotation of these sequences has become a major challenge in the field of Bioinformatics. The dispersion in the data makes this task even tougher. The authors illustrate in this paper a simple yet efficient way for functionally characterizing a novel enzyme by the application of support vector machines. The best accuracy gained by this method on generalization test is 91.55% with Mathew's Correlation Coefficient (MCC) of 0.63. The method was further validated by three different types of testing. The resulting accuracy for the LOO estimate was found to be 91.05% with MCC of 0.62 henceforth resolving any over fitting of data that may be present in the instance sets.