Prediction of O-glycosylation Sites Using Random Forest and GA-Tuned PSO Technique.

ABSTRACT: O-glycosylation is one of the main types of the mammalian protein glycosylation; it occurs on the particular site of serine (S) or threonine (T). Several O-glycosylation site predictors have been developed. However, a need to get even better prediction tools remains. One challenge in training the classifiers is that the available datasets are highly imbalanced, which makes the classification accuracy for the minority class to become unsatisfactory. In our previous work, we have proposed a new classification approach, which is based on particle swarm optimization (PSO) and random forest (RF); this approach has considered the imbalanced dataset problem. The PSO parameters setting in the training process impacts the classification accuracy. Thus, in this paper, we perform parameters optimizat

SUBMITTER: Hassan H 

PROVIDER: S-EPMC4494626 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature