Prediction of glycosylation sites using random forests
Open Access
- 27 November 2008
- journal article
- research article
- Published by Springer Nature in BMC Bioinformatics
- Vol. 9 (1) , 500
- https://doi.org/10.1186/1471-2105-9-500
Abstract
Post translational modifications (PTMs) occur in the vast majority of proteins and are essential for function. Prediction of the sequence location of PTMs enhances the functional characterisation of proteins. Glycosylation is one type of PTM, and is implicated in protein folding, transport and function.Keywords
This publication has 32 references indexed in Scilit:
- Prediction of mucin-type O-glycosylation sites in mammalian proteins using the composition of k-spaced amino acid pairsBMC Bioinformatics, 2008
- Glycosylation site prediction using ensembles of Support Vector Machine classifiersBMC Bioinformatics, 2007
- Predicting O-glycosylation sites in mammalian proteins by using SVMsComputational Biology and Chemistry, 2006
- Prediction of protein–protein interactions using random decision forest frameworkBioinformatics, 2005
- Identifying SNPs predictive of phenotype using random forestsGenetic Epidemiology, 2004
- Prediction of post‐translational glycosylation and phosphorylation of proteins from the amino acid sequenceProteomics, 2004
- Database Analysis of O-Glycosylation Sites in ProteinsBiophysical Journal, 2001
- Prediction of Potential GPI-modification Sites in Proprotein SequencesJournal of Molecular Biology, 1999
- Protein secondary structure prediction based on position-specific scoring matrices 1 1Edited by G. Von HeijneJournal of Molecular Biology, 1999
- GlycosylationCurrent Opinion in Cell Biology, 1992