Predict collagen hydroxyproline sites using support vector machines.
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%.
Yang
Zheng Rong
ZR
School of Biosciences, University of Exeter, Exeter, United Kingdom. z.r.yang@ex.ac.uk
D000465:Algorithms; D000595:Amino Acid Sequence; D001185:Artificial Intelligence; D003094:Collagen / Q000737:chemistry*; D006909:Hydroxyproline / Q000737:chemistry*; D008433:Mathematics; D008956:Models, Chemical; D008969:Molecular Sequence Data; D010455:Peptides / Q000737:chemistry / Q000235:genetics; D012372:ROC Curve; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D020539:Sequence Analysis, Protein
D010455:Peptides; D003094:Collagen; D006909:Hydroxyproline