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Int. J. Mol. Sci. 2017, 18(12), 2718; https://doi.org/10.3390/ijms18122718

Protein Subcellular Localization with Gaussian Kernel Discriminant Analysis and Its Kernel Parameter Selection

1,†,* , 1,†
,
1,* , 2,* , 1
and
1
1
Department of Computer Science and Engineering, School of Information Science and Engineering, Yunnan University, Kunming 650504, China
2
School of Statistics and Mathematics, Yunnan University of Finance and Economics, Kunming 650221, China
These authors contributed equally to this work.
*
Authors to whom correspondence should be addressed.
Received: 16 October 2017 / Revised: 4 December 2017 / Accepted: 5 December 2017 / Published: 15 December 2017
(This article belongs to the Special Issue Special Protein Molecules Computational Identification)
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Abstract

Kernel discriminant analysis (KDA) is a dimension reduction and classification algorithm based on nonlinear kernel trick, which can be novelly used to treat high-dimensional and complex biological data before undergoing classification processes such as protein subcellular localization. Kernel parameters make a great impact on the performance of the KDA model. Specifically, for KDA with the popular Gaussian kernel, to select the scale parameter is still a challenging problem. Thus, this paper introduces the KDA method and proposes a new method for Gaussian kernel parameter selection depending on the fact that the differences between reconstruction errors of edge normal samples and those of interior normal samples should be maximized for certain suitable kernel parameters. Experiments with various standard data sets of protein subcellular localization show that the overall accuracy of protein classification prediction with KDA is much higher than that without KDA. Meanwhile, the kernel parameter of KDA has a great impact on the efficiency, and the proposed method can produce an optimum parameter, which makes the new algorithm not only perform as effectively as the traditional ones, but also reduce the computational time and thus improve efficiency. View Full-Text
Keywords: protein subcellular localization; kernel parameter selection; kernel discriminant analysis (KDA); Gaussian kernel function; dimension reduction protein subcellular localization; kernel parameter selection; kernel discriminant analysis (KDA); Gaussian kernel function; dimension reduction
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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 (CC BY 4.0).
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Wang, S.; Nie, B.; Yue, K.; Fei, Y.; Li, W.; Xu, D. Protein Subcellular Localization with Gaussian Kernel Discriminant Analysis and Its Kernel Parameter Selection. Int. J. Mol. Sci. 2017, 18, 2718.

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