A methodology for the prediction of Embryophyta protein functions using mismatch kernels

This work implements a type of string kernel called Mismatch kernel, together with a methodology involving Support Vector Machines (SVM) for solving 14 molecular function classification problems of land plants (Embryophyta). The implemented methodology uses metaheuristic bio-inspired algorithms for finding optimal hyperparameters of the SVM, to solve the problem of imbalanced data class weights are also taken as hyperparameters in order to avoid sampling methods. The results were compared with the RBF (radial basis function) kernel over the same methodology. Geometric mean between specificity and sensitivity was used as the performance measure, showing that string kernels are the most suitable choice for the problem at hand.