Prediction of O-Linked Glycosylation Sites in Protein by Independent Component Analysis

摘要：

Glycosylation is one of the most important post translation modifications steps in eukaryotic cell. In this paper, we propose a new approach based on independent component analysis (ICA) for prediction O-linked glycosylation site and pattern analysis. Principal component analysis (PCA) is first used to find significant uncorrelated components, and then ICA is used to extract independent components to construct a subspace (main basis) of protein sequence. The prediction is viewed as a 2-classes classification problem. The test protein vector is projected to each subspace. The protein sequence is classified into the nearest class by calculating the distance between the test vector and its projection on the subspace. The prediction accuracy of our proposed new approach is higher than that of other su bspace methods based on PCA

关键词： O-glycosylation pattern analysis positional probability function independent component analysis

作者: Chu-Zheng Wang Xiao-Feng Tan Yen-Wei Chen Masahiro Ito Ikuko Nishikawa

作者单位: College of Computer and Information Engineering Central South University of Forestry and Technology College oflnformation Science and Eng Ritsumeikan Univ,Shiga, Japan

会议类型: 国际会议

会议名称: 2010 2nd International Conference on Signal Processing System(2010年信号处理系统国际会议 ICSPS 2010)

会议地点: 大连

会议语种:英文

页码: 206-210

在线出版日期: 2010-07-05（万方平台首次上网日期，不代表论文的发表时间）