期刊文献+

基于核Fisher判别分析的蛋白质氧链糖基化位点的预测 被引量:5

Prediction of O-glycosylation sites in protein sequence by kernel Fisher discriminant analysis
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摘要 以各种窗口长度的蛋白质样本序列为研究对象,实验样本用稀疏编码方式编码,使用核Fisher判别分析(KFDA)的方法来预测蛋白质氧链糖基化位点。首先通过非线性映射(由核函数隐含定义)将样本映射到特征空间,然后在特征空间中用Fisher判别分析进行分类。进一步,用多数投票策略对各种窗口下的分类器进行组合以综合多个窗口的优势。实验结果表明,使用组合KFDA的方法预测的效果优于FDA和PCA以及单个KFDA分类器的预测效果,预测准确率为86.5%。 To predict the O-glycosylation sites in protein sequence, the method of Kernel Fisher Discriminant Analysis (KFDA) was proposed under various window sizes. Encoded by the sparse coding, the samples were first mapped onto a feature space implicitly defined by a kernel function, and then they were classified into two classes in the feature space by Fisher discriminant analysis. Furthermore, the majority-vote scheme was used to combine all the pre-classifiers to improve the prediction performance. The results indicate that the performance of ensembles of KFDA is better than that of FDA, PCA and pre-classifier. The prediction accuracy is about 86.5%.
出处 《计算机应用》 CSCD 北大核心 2010年第11期2959-2961,共3页 journal of Computer Applications
基金 陕西省自然科学基金资助项目(2010JQ1013) 陕西省教育厅科学研究计划项目(2010JK896 09JK809) 咸阳师范学院专项科研基金资助项目(07XSYK107) 咸阳师范学院大学生科研训练项目(09101)
关键词 糖基化 蛋白质 核FISHER判别分析 特征 glycosylation protein Kernel Fisher Discriminant Analysis (KFDA) feature
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参考文献8

  • 1NISHIKAWA I, SAKAMOTO H, NOUNO I, et al. Prediction of the O-glycosylation sites in protein by layered neural networks and support vector machines [ M]. Berlin: Springer, 2006:953 -960.
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同被引文献22

  • 1刘佳,王威,杨建军.核Fisher判别分析方法在项目评估中的应用[J].武汉理工大学学报(信息与管理工程版),2006,28(2):131-134. 被引量:8
  • 2高湘萍,许丹,吴小培.基于核Fisher判别分析的意识任务识别新方法[J].计算机技术与发展,2006,16(9):82-84. 被引量:6
  • 3Nishikawa I, Sakamoto H, Nouno I, et al. Prediction of the O-glyco- sylation sites in protein by layered neural networks and support vector machines. Lecture Notes in Artificial Intelligence, 2006; LNAI (4252) :953-960.
  • 4Kenta S, Nobuyoshi N, Yasubumi S. Support vector machines pre- diction of N- and O-glyeosylation sites using whole sequenee informa- tion and subeellular loealizition. IPSJ Transactions on Bioinformatics, 2009; (2) :25-35.
  • 5LI S. Predicting O-glyeosylation sites in mammalian proteins by using SVMs. Computational Biology and Chemistry,2006 ;30:203-208.
  • 6Chen Yongzi. Prediction of mucin-type O-Glyeosylation sites in mam- maliam protein using the composition of k-spaced amino acid pairs. BMC Bioinformatics ,2008 ;9 : 101-112.
  • 7Zhou Knn, Ai Chunzhi, Dong Peipei, et al. A novel model to predict O-glycosylation sites using a highly unbalanced dataset. Glyeoeon- jugate Journal ,2012 ;29(7 ) :551-564.
  • 8任苏娅.基于改进的PCA和ICA算法的掌纹识别研究.北京:北京交通大学,2007.
  • 9Scholkopf B, Aaexander S, MOiler K R. Nonlinear component analy- sis as a kernel eigenvalue problem. Neural Computation, 1998 ; 10 (5) :1299-1319.
  • 10Yang Xuemei, Cui Xuewei, Yang Xuezhu. Prediction of O-glycosy- lation sites in protein sequence by kernel principal component analy- sis. Proceedings of CASoN, 2010;267-270.

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