Combining classifiers to predict gene function in Arabidopsis thaliana using large-scale gene expression measurements

Background: Arabidopsis thaliana is the model species of current plant genomic research with a genome size of 125 Mb and approximately 28,000 genes. The function of half of these genes is currently unknown. The purpose of this study is to infer gene function in Arabidopsis using machine-learning algorithms applied to large-scale gene expression data sets, with the goal of identifying genes that are potentially involved in plant response to abiotic stress. Results: Using in house and publicly available data, we assembled a large set of gene expression measurements for A. thaliana. Using those genes of known function, we first evaluated and compared the ability of basic machine-learning algorithms to predict which genes respond to stress. Predictive accuracy was measured using ROC50 and precision curves derived through cross validation. To improve accuracy, we developed a method for combining these classifiers using a weighted-voting scheme. The combined classifier was then trained on g...

• Type: Journal
• Year: 2007
• Where: BMCBI
• Authors: Hui Lan, Rachel Carson, Nicholas J. Provart, Anthony J. Bonner
• Keywords: BMCBI 2007, Gene Expression, Gene Expression Measurements, Large-scale Gene Expression