title: Combining classifiers to predict gene function in Arabidopsis thaliana using large-scale gene expression measurements.
abstract: 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 genes of known function and applied to genes of unknown function, identifying genes that potentially respond to stress. Visual evidence corroborating the predictions was obtained using electronic Northern analysis. Three of the predicted genes were chosen for biological validation. Gene knockout experiments confirmed that all three are involved in a variety of stress responses. The biological analysis of one of these genes (At1g16850) is presented here, where it is shown to be necessary for the normal response to temperature and NaCl.
  
  
  CONCLUSIONS
  Supervised learning methods applied to large-scale gene expression measurements can be used to predict gene function. However, the ability of basic learning methods to predict stress response varies widely and depends heavily on how much dimensionality reduction is used. Our method of combining classifiers can improve the accuracy of such predictions - in this case, predictions of genes involved in stress response in plants - and it effectively chooses the appropriate amount of dimensionality reduction automatically. The method provides a useful means of identifying genes in A. thaliana that potentially respond to stress, and we expect it would be useful in other organisms and for other gene functions.
authors: Department of Computer Science, University of Toronto, 40 St George St, Toronto, ON M5S 2E4, Canada. lanhui@cs.toronto.edu
authors: Hui
authors: H
authors: Lan
authors: Rachel
authors: R
authors: Carson
authors: Nicholas J
authors: NJ
authors: Provart
authors: Anthony J
authors: AJ
authors: Bonner
mesh_terms: D017360:Arabidopsis / Q000235:genetics* / Q000378:metabolism; D001185:Artificial Intelligence; D016000:Cluster Analysis; D003080:Cold Temperature; D020869:Gene Expression Profiling; D018506:Gene Expression Regulation, Plant; D017343:Genes, Plant; D011237:Predictive Value of Tests; D012372:ROC Curve; D012680:Sensitivity and Specificity; D017422:Sequence Analysis, DNA; D012965:Sodium Chloride
chemical_list: D012965:Sodium Chloride