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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.


Case study using glycoprotein P02724.
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Related In: Results  -  Collection


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f4-bbi-9-2015-103: Case study using glycoprotein P02724.

Mentions: To further illustrate the performance of the GA-tuned PSO + RF predictor, we performed a case study of one protein extracted from UniProt32 benchmark database. The protein was glycophorin-A (GYPA, UniProt ID: P02724), a major intrinsic membrane protein with a high proportion of O-glycosylated residues in erythrocytes. It has 16 experimentally verified O-linked glycosylation sites. Our predictor could predict all of those sites (labeled with P as shown in Fig. 4). These results suggest that GA-tuned PSO + RF predictor can be a useful tool for in silico glycosylation site prediction.


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

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

Case study using glycoprotein P02724.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

f4-bbi-9-2015-103: Case study using glycoprotein P02724.
Mentions: To further illustrate the performance of the GA-tuned PSO + RF predictor, we performed a case study of one protein extracted from UniProt32 benchmark database. The protein was glycophorin-A (GYPA, UniProt ID: P02724), a major intrinsic membrane protein with a high proportion of O-glycosylated residues in erythrocytes. It has 16 experimentally verified O-linked glycosylation sites. Our predictor could predict all of those sites (labeled with P as shown in Fig. 4). These results suggest that GA-tuned PSO + RF predictor can be a useful tool for in silico glycosylation site prediction.

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.