Top-Down Hierarchical Ensembles of Classifiers for Predicting G-Protein-Coupled-Receptor Functions

Despite the recent advances in Molecular Biology, the function of a large amount of proteins is still unknown. An approach that can be used in the prediction of a protein function consists of searching against secondary databases, also known as signature databases. Different strategies can be applied to use protein signatures in the prediction of function of proteins. A sophisticated approach consists of inducing a classification model for this prediction. This paper applies five hierarchical classification methods based on the standard Top-Down approach and one hierarchical classification method based on a new approach named Top-Down Ensembles based on the hierarchical combination of classifiers to three different protein functional classification datasets that employ protein signatures. The algorithm based on the Top-Down Ensembles approach presented slightly better results than the other algorithms, indicating that combinations of classifiers can improve the performance of hierarchical classification models.