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In the paper, we presented a new computational framework based on kernel sparse representation theory to predict S-nitrosylation sites.
A webserver for the prediction of S-nitrosylation sites based on kernel sparse representation classification and minimum Redundancy Maximum Relevance algorithm is available at http://www.zhni.net/snopred/index.html.html
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.
G. Huang et al. developed a method to predict S-nitrosylation modification sites based on kernel sparse representation classification and mRMR Algorithm.
Finally, a webserver for the prediction of S-nitrosylation sites based on kernel sparse representation classification and minimum Redundancy Maximum Relevance algorithm is available at http://www.zhni.net/snopred/index.html.html
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Additionally, PDF estimators based on kernel functions are also developed.
The third feature, based on kernel density estimation (KDE), is the "KDE Peak" value.
However, some other feature extraction methods have put forward new ways which are based on kernels.
Methods based on kernelized score functions are very fast (their time complexity is approximately linear in sparse graphs, once the kernel matrix is computed) [31], and their accuracy is at least comparable with state-of-the-art gene prioritization methods [40].
The construction of kernel feature space based on the sparse codes of training samples provides promising implement of curvilinear coordinate system along the flow of nonlinear feature.
Many of them take advantage of the existing relationships between the DCT and various other fast transforms, while others are based on the sparse factorization of the DCT kernel.
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