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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.


PSO for undersampling.
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f1-bbi-9-2015-103: PSO for undersampling.

Mentions: The subsets from the majority class that can create more accurate classification are favored and optimized in each PSO iteration. Samples from the last PSO iteration are ranked by their selection frequency in the optimization process. The samples from the majority class that are most frequently included in the optimized subsets are selected to match the number of minority samples to generate a balanced dataset. See Supplementary Files Imbalanced.arff and PsoUnder-sampled.arff for imbalanced and PSO undersampled datasets, respectively. The balanced dataset has been used for training a RF.19 classifier implemented in WEKA,24 a widely used machine learning workbench in bioinformatics implemented in Java. Figure 1 summarizes the PSO for undersampling technique.


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

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

PSO for undersampling.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

f1-bbi-9-2015-103: PSO for undersampling.
Mentions: The subsets from the majority class that can create more accurate classification are favored and optimized in each PSO iteration. Samples from the last PSO iteration are ranked by their selection frequency in the optimization process. The samples from the majority class that are most frequently included in the optimized subsets are selected to match the number of minority samples to generate a balanced dataset. See Supplementary Files Imbalanced.arff and PsoUnder-sampled.arff for imbalanced and PSO undersampled datasets, respectively. The balanced dataset has been used for training a RF.19 classifier implemented in WEKA,24 a widely used machine learning workbench in bioinformatics implemented in Java. Figure 1 summarizes the PSO for undersampling technique.

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.