期刊文献+
共找到1篇文章
< 1 >
每页显示 20 50 100
Gene Selection for Classifications Using Multiple PCA with Sparsity
1
作者 Yanwei Huang Liqing Zhang 《Tsinghua Science and Technology》 SCIE EI CAS 2012年第6期659-665,共7页
A gene selection algorithm was developed using Multiple Principal Component Analysis with Sparsity (MSPCA). The MSPCA algorithm is used to analyze normal and disease gene expression samples and to set these componen... A gene selection algorithm was developed using Multiple Principal Component Analysis with Sparsity (MSPCA). The MSPCA algorithm is used to analyze normal and disease gene expression samples and to set these component Ioadings to zero if they are smaller than a threshold for sparse solutions. Next, genes with zero Ioadings across all samples (both normal and disease) are removed before extracting feature genes. Feature genes are genes that contribute differentially to variations in normal and disease samples and, thus, can be used for classification. The MSPCA is applied to three microarray datasets to select feature genes with a linear support vector machine to evaluate its performance. This method is compared with several previous gene selection results to show that this MSPCA gene selection algorithm has good classification accuracy and model stability. 展开更多
关键词 microarray gene expression gene selection Multiple Principal Component analysis with sparsity (MSPCA) sparse
原文传递
上一页 1 下一页 到第
使用帮助 返回顶部