Predict collagen hydroxyproline sites using support vector machines.
Journal of Computational Biology
Mary Ann Liebert
Collagen hydroxyproline is an important posttranslational modification activity because of its close relationship with various diseases and signaling activities. However, there is no study to date for constructing models for predicting collagen hydroxyproline sites. Support vector machines with two kernel functions (the identity kernel function and the bio-kernel function) have been used for constructing models for predicting collagen hydroxyproline sites in this study. The models are constructed based on 37 sequences collected from NCBI. Peptide data are generated using a sliding window with various sizes to scan the sequences. Fivefold cross-validation is used for model evaluation. The best model has specificity of 70% and sensitivity of 90%.
addresses: School of Biosciences, University of Exeter, Exeter, United Kingdom. firstname.lastname@example.org
types: Journal Article
This is a copy of an article published in the Journal of Computational Biology © 2009 copyright Mary Ann Liebert, Inc.; Journal of Computational Biology is available online at: http://online.liebertpub.com.
Journal of Computational Biology, 2009, Vol. 16, Issue 5, pp. 691 - 702
Place of publication