title: Predicting O-glycosylation sites in mammalian proteins by using SVMs.
abstract: O-glycosylation is one of the most important, frequent and complex post-translational modifications. This modification can activate and affect protein functions. Here, we present three support vector machines models based on physical properties, 0/1 system, and the system combining the above two features. The prediction accuracies of the three models have reached 0.82, 0.85 and 0.85, respectively. The accuracies of the three SVMs methods were evaluated by 'leave-one-out' cross validation. This approach provides a useful tool to help identify the O-glycosylation sites in mammalian proteins. An online prediction web server is available at http://www.biosino.org/Oglyc.
authors.affiliation: Bioinformatics Center, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, China.
authors.forename: Sujun
authors.initials: S
authors.lastname: Li
authors.forename: Boshu
authors.initials: B
authors.lastname: Liu
authors.forename: Rong
authors.initials: R
authors.lastname: Zeng
authors.forename: Yudong
authors.initials: Y
authors.lastname: Cai
authors.forename: Yixue
authors.initials: Y
authors.lastname: Li
mesh_terms: D000818:Animals; D001185:Artificial Intelligence; D001665:Binding Sites; D006023:Glycoproteins / Q000737:chemistry*; D006031:Glycosylation; D006801:Humans; D020407:Internet; D011499:Protein Processing, Post-Translational
chemical_list: D006023:Glycoproteins