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Predicting O-glycosylation sites in mammalian proteins by using SVMs

Paper ID Volume ID Publish Year Pages File Format Full-Text
15464 1415 2006 6 PDF Available
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.

Keywords
Post-translational modification; Bioinformatics; Prediction; O-glycosylation; Support vector machines
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Predicting O-glycosylation sites in mammalian proteins by using SVMs
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Publisher
Database: Elsevier - ScienceDirect
Journal: Computational Biology and Chemistry - Volume 30, Issue 3, June 2006, Pages 203–208
Authors
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Subjects
Physical Sciences and Engineering Chemical Engineering Bioengineering
Get Full-Text Now
Don't Miss Today's Special Offer
Price was $35.95
You save - $31
Price after discount Only $4.95
100% Money Back Guarantee
Full-text PDF Download
Online Support
Any Questions? feel free to contact us