Prediction of O-Glycosylation Sites in Proteins using PSO-Based Data Balancing and Random Forest

O-glycosylation of mammalian proteins is one of the most important post-translational modifications (PTMs). Hence, there is significant interest in the development of computational methods for reliable prediction of O-Glycosylation sites from amino acid sequences. One particular challenge in training the classifiers comes from the fact that the available dataset is highly imbalanced, which makes the classification performance for the minority class becomes unsatisfactory. Traditional sampling approaches generally rely on random re-sampling from a given dataset. However, these methods cannot utilize all the information available in the training set and it increases the false positive rate. This paper proposes a new approach for predicting the O-glycosylation sites which is based on Particle Swarm optimization (PSO) and Random Forest (RF). PSO is used as evolutionary under-sampling technique for balancing the dataset, and Random Forest is used as a classifier. The results obtained from the proposed approach and other related researches, demonstrate that the proposed approach outperforms the performance of other approaches for the experimented dataset. [Hebatallah A. Hassan, M. B. Abdelhalim, Amr Badr. Prediction of O-Glycosylation Sites in Proteins using PSOBased Data Balancing and Random Forest. Life Sci J 2014;11(12):1019-1025]. (ISSN:1097-8135). http://www.lifesciencesite.com. 175