A web server for identifying S-glutathionylation sites. GSHSite is based on a statistical method for identifying S-glutathionylation sites and potential consensus motifs by maximal dependence decomposition (MDD). With the application of MDD, a large group of aligned sequences can be moderated into subgroups that capture the most significant dependencies between positions. By further evaluation using five-fold cross-validation, the support vector machine (SVM) models trained with MDD-clustered subgroups could improve predictive accuracy when compared to the model without MDD clustering. Moreover, the experimental S-glutathionylation data from published database (independent set) are used to test the effectiveness of the models in cross-validation.
Institute of Chemistry, Academia Sinica, Taipei, Taiwan; Department of Computer Science and Engineering, Yuan Ze University, Taoyuan, Taiwan; Innovation Center for Big Data and Digital Convergence, Yuan Ze University, Taoyuan, Taiwan
GSHSite funding source(s)
The authors sincerely appreciate the Ministry of Science and Technology of Taiwan for financially supporting this research under Contract Number MOST 103-2221-E-155-020-MY3 and 103-2633-E-155-002 and 100-2628-M-001-003-MY4.