GPP specifications


Unique identifier OMICS_07056
Name GPP
Alternative name Glycosylation Prediction Program
Interface Web user interface
Restrictions to use None
Computer skills Basic
Stability Stable
Maintained Yes

Publication for Glycosylation Prediction Program

GPP citations


Structure based Comparative Analysis and Prediction of N linked Glycosylation Sites in Evolutionarily Distant Eukaryotes

PMCID: 3914773
PMID: 23459159
DOI: 10.1016/j.gpb.2012.11.003

[…] accuracy; NetNGlyc ( uses artificial neural networks that examine the sequence context of Asn-X-Ser/Thr sequons with an overall accuracy of 76%. In addition, GPP uses the random forest algorithm and pairwise patterns to predict glycosylation sites with an accuracy of 90.8% for Ser sites, 92.0% for Thr sites and 92.8% for Asn sites. It is important to note […]


Bioinformatics and molecular modeling in glycobiology

PMCID: 2912727
PMID: 20364395
DOI: 10.1007/s00018-010-0352-4

[…] ation sites; big-Pi [] employs scoring functions based on amino acid properties; GPI-SOM [] uses a Kohonen map; CKSAAP_OGlySite [], and EnsembleGly [] use a Support Vector Machine based approach; and GPP [], the currently best performing predication tool, uses a hybrid combinatorial and statistical learning approach based on random forests. Training datasets for the statistical learning approaches […]

GPP institution(s)
School of Chemistry, University of Nottingham, University Park, Nottingham, UK

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