A class-information-based penalized matrix decomposition for identifying plants core genes responding to abiotic stresses.

ABSTRACT: In terms of making genes expression data more interpretable and comprehensible, there exists a significant superiority on sparse methods. Many sparse methods, such as penalized matrix decomposition (PMD) and sparse principal component analysis (SPCA), have been applied to extract plants core genes. Supervised algorithms, especially the support vector machine-recursive feature elimination (SVM-RFE) method, always have good performance in gene selection. In this paper, we draw into class information via the total scatter matrix and put forward a class-information-based penalized matrix decomposition (CIPMD) method to improve the gene identification performance of PMD-based method. Firstly, the total scatter matrix is obtained based on different samples of the gene expression data. Secondly,

SUBMITTER: Liu JX 

PROVIDER: S-EPMC4152128 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature