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BioMed Research International
Volume 2014, Article ID 438341, 10 pages
Research Article

Prediction of S-Nitrosylation Modification Sites Based on Kernel Sparse Representation Classification and mRMR Algorithm

1Institute of Systems Biology, Shanghai University, Shanghai 200444, China
2Department of Mathematics, Shaoyang University, Shaoyang, Hunan 422000, China
3School of Biomedical Engineering, Shanghai Jiaotong University, Shanghai 200240, China
4Shanghai Center for Bioinformation Technology, Shanghai 200235, China
5Graduate School of the Chinese Academy of Sciences, Beijing 100049, China
6State Key Laboratory of Medical Genomics, Institute of Health Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
7East China Normal University Software Engineering Institute, Shanghai 200062, China
8Department of Biomedical Engineering, Tianjin University, Tianjin Key Lab of BME Measurement, Tianjin 300072, China
9Key Laboratory of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China

Received 19 June 2014; Accepted 23 July 2014; Published 12 August 2014

Academic Editor: Tao Huang

Copyright © 2014 Guohua Huang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Supplementary Material

supplementary material S1:lists 645 protein sequences containing S-nitrosylation sites.

supplementary material S2:lists both of the protein sequence Uniprot ids and S-nitrosylation site locations in the training and testing sets, respectively.

supplementary material S3:presents the training set with 666 features.

supplementary material S4:presents the testing set with 666 features.

supplementary material S5:lists the order of the 666 features ranked by the mRMR algorithm.

  1. Supplementary Materials