Prediction of drought-resistant genes in Arabidopsis thaliana using SVM-RFE.

ABSTRACT:

Background

Identifying genes with essential roles in resisting environmental stress rates high in agronomic importance. Although massive DNA microarray gene expression data have been generated for plants, current computational approaches underutilize these data for studying genotype-trait relationships. Some advanced gene identification methods have been explored for human diseases, but typically these methods have not been converted into publicly available software tools and cannot be applied to plants for identifying genes with agronomic traits.

Methodology

In this study, we used 22 sets of Arabidopsis thaliana gene expression data from GEO to predict the key genes involved in water tolerance. We applied an SVM-RFE (Support Vector Machine-Recursive Feature Elimination) fea

SUBMITTER: Liang Y 

PROVIDER: S-EPMC3137602 | biostudies-literature | 2011

REPOSITORIES: biostudies-literature