Identifies S-nitrosylation (SNO) sites from protein sequences. Using both informative features and an elaborate feature selection scheme, PSNO achieves good prediction performance with a mean Mathews correlation coefficient (MCC) value of about 0.5119 on the training dataset using 10-fold cross-validation. These results indicate that PSNO can be used as a competitive predictor among the state-of-the-art SNOs prediction tools.
School of Computer Science and Information Technology, Northeast Normal University, Changchun, China
PSNO funding source(s)
This work was supported by the Fundamental Research Funds for the Central Universities (Grant No. 12QNJJ005, 14QNJJ029), the Postdoctoral Science Foundation of China (Grant No. 2014M550166, 111900166), and the Research Fund for the Doctoral Program of Higher Education of China (Grant No. 20130043110016).