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Protein phosphorylation site prediction via feature discovery support vector machine.
| Content Provider | CiteSeerX |
|---|---|
| Author | Shi, Yi Yuan, Bo Lin, Guohui Schuurmans, Dale |
| Abstract | Abstract—Protein phosphorylation/dephosphorylation is the central mechanism of post-translational modification which regulates cellular responses and phenotypes. Due to the efficiency and resource constraints of the in vivo methods for identifying phosphorylation sites, there is a strong motivation to computationally predict potential phosphorylation sites. In this work, we propose to use a unique set of features to represent the peptides surrounding the amino acid sites of interest and use feature selection support vector machine to predict whether the serine/threonine sites are potentially phosphorylable, as well as selecting important features that may lead to phosphorylation. Experimental results indicate that the new features and the prediction method can more effectively predict protein phosphorylation sites than the existing state of the art methods. The features selected by the our prediction model provides biological insights to the in vivo phosphorylation. I. |
| File Format | |
| Access Restriction | Open |
| Subject Keyword | Feature Discovery Support Vector Machine Protein Phosphorylation Site Prediction Amino Acid Site Art Method Unique Set Prediction Method Biological Insight Vivo Method Predict Protein Phosphorylation Site Resource Constraint New Feature Abstract Protein Phosphorylation Dephosphorylation Potential Phosphorylation Site Cellular Response Vivo Phosphorylation Phosphorylation Site Post-translational Modification Prediction Model Important Feature Serine Threonine Site Central Mechanism Strong Motivation Experimental Result |
| Content Type | Text |
