Research on Prediction of Protein Sub-cellular Location Based on KLDA with Combined Kernel Function

被引：0

作者：

Nie, Bing ^{[1
]}

Wang, Shunfang ^{[1
]}

Xu, Dongshu ^{[1
]}

机构：

[1] Yunnan Univ, Sch Informat Sci & Engn, Kunming 650504, Peoples R China

来源：

PROCEEDINGS OF 2015 4TH INTERNATIONAL CONFERENCE ON COMPUTER SCIENCE AND NETWORK TECHNOLOGY (ICCSNT 2015) | 2015年

基金：

中国国家自然科学基金;

关键词：

combined kernel function; kernel linear discriminant analysis; Gauss kernel function; polynomial kernel function; protein sub-cellular localization;

D O I：

暂无

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

To improve the accuracy of prediction of subcellular location, a new method using kernel linear discriminant analysis with combinational kernel function which is made up of the Gauss kernel function and the polynomial kernel function is used to the predict the sub-cellular location. In order to confirm the reliability of the research, the data used in this paper are from the standard data set included in Swiss-Prot database and the values of parameters for combined kernel function are determined reasonably. The results indicate that the proposed method with combined kernel function is more efficient than the kernel linear discriminant analysis algorithm with traditional kernel functions in the prediction of sub-cellular location.

引用

页码：338 / 341

页数：4

共 19 条

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