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

Hassan H, Badr A, Abdelhalim MB - Bioinform Biol Insights (2015)

Bottom Line: However, a need to get even better prediction tools remains.Our proposed genetic algorithm-based approach has shown better performance in terms of area under the receiver operating characteristic curve against existing predictors.In addition, we implemented a glycosylation predictor tool based on that approach, and we demonstrated that this tool could successfully identify candidate glycosylation sites in case study protein.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science, College of Computing and Information Technology, Arab Academy for Science and Technology and Maritime Transport (AASTMT), Cairo, Egypt.

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 optimization for the PSO algorithm, based on genetic algorithm, in order to increase the classification accuracy. Our proposed genetic algorithm-based approach has shown better performance in terms of area under the receiver operating characteristic curve against existing predictors. In addition, we implemented a glycosylation predictor tool based on that approach, and we demonstrated that this tool could successfully identify candidate glycosylation sites in case study protein.

No MeSH data available.


GA evolutionary cycle.
© Copyright Policy - open-access
Related In: Results  -  Collection


getmorefigures.php?uid=PMC4494626&req=5

f2-bbi-9-2015-103: GA evolutionary cycle.

Mentions: Figure 2 illustrates the GA evolutionary cycle. Crossover allows new solutions in the search space to be explored; it is a random mechanism for exchanging genes between two chromosomes using the one-point crossover, two-point crossover, or homolog crossover. In mutation, the genes may occasionally be altered, ie, in binary code genes, changing genes code from 1 to 0 or vice versa.26,27 Offspring replaces the old population using the elitism or diversity replacement strategy and forms a new population in the next generation. The evolutionary process operates many generations until the termination condition is satisfied.


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

Hassan H, Badr A, Abdelhalim MB - Bioinform Biol Insights (2015)

GA evolutionary cycle.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4494626&req=5

f2-bbi-9-2015-103: GA evolutionary cycle.
Mentions: Figure 2 illustrates the GA evolutionary cycle. Crossover allows new solutions in the search space to be explored; it is a random mechanism for exchanging genes between two chromosomes using the one-point crossover, two-point crossover, or homolog crossover. In mutation, the genes may occasionally be altered, ie, in binary code genes, changing genes code from 1 to 0 or vice versa.26,27 Offspring replaces the old population using the elitism or diversity replacement strategy and forms a new population in the next generation. The evolutionary process operates many generations until the termination condition is satisfied.

Bottom Line: However, a need to get even better prediction tools remains.Our proposed genetic algorithm-based approach has shown better performance in terms of area under the receiver operating characteristic curve against existing predictors.In addition, we implemented a glycosylation predictor tool based on that approach, and we demonstrated that this tool could successfully identify candidate glycosylation sites in case study protein.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science, College of Computing and Information Technology, Arab Academy for Science and Technology and Maritime Transport (AASTMT), Cairo, Egypt.

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 optimization for the PSO algorithm, based on genetic algorithm, in order to increase the classification accuracy. Our proposed genetic algorithm-based approach has shown better performance in terms of area under the receiver operating characteristic curve against existing predictors. In addition, we implemented a glycosylation predictor tool based on that approach, and we demonstrated that this tool could successfully identify candidate glycosylation sites in case study protein.

No MeSH data available.