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

Content Provider	PubMed Central
Author	Huang, Guohua Lin, Lu Feng, Kaiyan Zhao, Jun Zhang, Yuchao Xu, Yaochen Zhang, Ning Li, Bi-qing Huang, Weiping Cai, Yu-dong
Copyright Year	2014
Abstract	Protein S-nitrosylation plays a very important role in a wide variety of cellular biological activities. Hitherto, accurate prediction of S-nitrosylation sites is still of great challenge. In this paper, we presented a framework to computationally predict S-nitrosylation sites based on kernel sparse representation classification and minimum Redundancy Maximum Relevance algorithm. As much as 666 features derived from five categories of amino acid properties and one protein structure feature are used for numerical representation of proteins. A total of 529 protein sequences collected from the open-access databases and published literatures are used to train and test our predictor. Computational results show that our predictor achieves Matthews' correlation coefficients of 0.1634 and 0.2919 for the training set and the testing set, respectively, which are better than those of k-nearest neighbor algorithm, random forest algorithm, and sparse representation classification algorithm. The experimental results also indicate that 134 optimal features can better represent the peptides of protein S-nitrosylation than the original 666 redundant features. Furthermore, we constructed an independent testing set of 113 protein sequences to evaluate the robustness of our predictor. Experimental result showed that our predictor also yielded good performance on the independent testing set with Matthews' correlation coefficients of 0.2239.
Related Links	http://dx.doi.org/10.1155/2014/438341
Starting Page	438341
File Format	PDF
ISSN	23146133
e-ISSN	23146141
Journal	BioMed Research International
Volume Number	2014
Language	English
Publisher	Hindawi Publishing Corporation
Publisher Date	2014-01-01
Access Restriction	Open
Rights Holder	Hindawi Publishing Corporation
Subject Keyword	Research in Higher Education
Content Type	Text
Resource Type	Article
Subject	Immunology and Microbiology Medicine Biochemistry, Genetics and Molecular Biology

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