O-linked Glycosylation Site Prediction Using Ensemble of Graphical Models

Abstract

Prediction of O-linked glycosylation sites in proteins is a challenging problem. In this paper, we introduced a new method to predict glycosylation sites in proteins. First, we built a Markov random field (MRF) to represent the sequence position relationship and model the underlying distribution of glycosylation sites. We then considered glycosylation site prediction as a class imbalance problem and employed the AdaBoost algorithm to improve the predictive performance of the classifier. We applied our method to two types of proteins: the transmembrane (TM) proteins and the non-transmembrane (non-TM) proteins. We showed that for both datasets, our methods outperform existing methods. We also showed that the performance of the system was improved significantly with the help of AdaBoost.