Prediction of S-Nitrosylation Modification Sites Based on Kernel Sparse Representation Classification and mRMR Algorithm
Joint Authors
Zhang, Yuchao
Feng, Kaiyan
Zhang, Ning
Lu, Lin
Huang, Weiping
Huang, Guohua
Cai, Yu-Dong
Xu, Yaochen
Zhao, Jun
Li, Bi-Qing
Source
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-08-11
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Huang, Guohua& Lu, Lin& Feng, Kaiyan& Zhao, Jun& Zhang, Yuchao& Xu, Yaochen…[et al.]. 2014. Prediction of S-Nitrosylation Modification Sites Based on Kernel Sparse Representation Classification and mRMR Algorithm. BioMed Research International،Vol. 2014, no. 2014, pp.1-10.
https://search.emarefa.net/detail/BIM-472340
Modern Language Association (MLA)
Huang, Guohua…[et al.]. Prediction of S-Nitrosylation Modification Sites Based on Kernel Sparse Representation Classification and mRMR Algorithm. BioMed Research International No. 2014 (2014), pp.1-10.
https://search.emarefa.net/detail/BIM-472340
American Medical Association (AMA)
Huang, Guohua& Lu, Lin& Feng, Kaiyan& Zhao, Jun& Zhang, Yuchao& Xu, Yaochen…[et al.]. Prediction of S-Nitrosylation Modification Sites Based on Kernel Sparse Representation Classification and mRMR Algorithm. BioMed Research International. 2014. Vol. 2014, no. 2014, pp.1-10.
https://search.emarefa.net/detail/BIM-472340
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-472340