Abstract
O-glycosylation means that sugar transferred to the protein. It can adjust the function of protein. To obtain a higher prediction accuracy of O-glycosylation sites, we used a method of Kernel Local Fisher Discriminant Analysis (KLFDA). The original data are projected into the subspace constructed by KLFDA, and the local feature vectors are extracted. Then the prediction (classification) is done in feature subspace by support vector machines (SVM). The results of experiments show that compared with LFDA, FDA, KPCA, PCA and ICA, the prediction accuracy of KLFDA is the best.
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Acknowledgment
This work is partially supported by the Scientific Research Project of Education Department of Shaanxi Province (No. 2013JK1125), the Nature Science Fund Project of Shaanxi Province (No. 2014JM1032), and the Science and Technology Project of National Bureau of Quality Inspection (No. 2013QK152).
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Yang, X., Sun, S. (2015). Kernel Local Fisher Discriminant Analysis-Based Prediction on Protein O-Glycosylation Sites Using SVM. In: Huang, DS., Han, K. (eds) Advanced Intelligent Computing Theories and Applications. ICIC 2015. Lecture Notes in Computer Science(), vol 9227. Springer, Cham. https://doi.org/10.1007/978-3-319-22053-6_73
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DOI: https://doi.org/10.1007/978-3-319-22053-6_73
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