ArticleBioinformatics and biology insights2015
Prediction of O-glycosylation Sites Using Random Forest and GA-Tuned PSO Technique.
Article in Bioinformatics and biology insights, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 14 citations in OpenAlex.
- Deglycosylation Differentially Regulates Weaned Porcine Gut Alkaline Phosphatase Isoform Functionality along the Longitudinal Axis.Pathogens (Basel, Switzerland) · 2023Article
- Prediction and risk stratification from hospital discharge records based on Hierarchical sLDA.BMC medical informatics and decision making · 2022Article
- Maximizing Depth of PTM Coverage: Generating Robust MS Datasets for Computational Prediction Modeling.Methods in molecular biology (Clifton, N.J.) · 2022Review
- Ridge regression estimated linear probability model predictions of O-glycosylation in proteins with structural and sequence data.BMC molecular and cell biology · 2019Article
- SVM-SulfoSite: A support vector machine based predictor for sulfenylation sites.Scientific reports · 2018Article
- CURE-SMOTE algorithm and hybrid algorithm for feature selection and parameter optimization based on random forests.BMC bioinformatics · 2017Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.