Identifying Disease Genes from Gene Expression Data Based on Singular Value Decomposition
Identification of disease genes that might anticipate the clinical behavior of human cancers is very important for understanding cancer pathogenesis. Computational analysis of disease gene from microarray data involves a search for gene subset that is able to discriminate cancer samples from normal samples, which is a challenging task due to a small number of samples compared to huge number of genes. In this paper, an algorithm (LRSVD) based on singular value decomposition and logistic regression is proposed to find genes that are associated with disease. LRSVD makes use of a threshold value to control the number of singular vectors; evaluates the contribution of each eigengene to the classifying accuracy by regression coefficients of logistic regression; and then ranks each gene by its discriminative power for two kinds of samples. The results on colon gene expression data indicate that LRSVD method with support vector machine (SVM) as a classifier is an encouraging method to identify disease genes.
singular value decomposition logistic regression microarray data
Huanping Zhang Xiaofeng Song Xiaobai Zhang
College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing, China Department of Biomedical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, C
国际会议
上海
英文
1755-1759
2011-10-15（万方平台首次上网日期，不代表论文的发表时间）