Term-tissue specific models for prediction of gene ontology biological processes using transcriptional profiles of aging in dros

Background: Predictive classification on the base of gene expression profiles appeared recently as an attractive strategy for identifying the biological functions of genes. Gene Ontology (GO) provides a valuable source of knowledge for model training and validation. The increasing collection of microarray data represents a valuable source for generating functional hypotheses of uncharacterized genes. Results: This study focused on using support vector machines (SVM) to predict GO biological processes from individual or multiple-tissue transcriptional profiles of aging in Drosophila melanogaster. Ten-fold cross validation was implemented to evaluate the prediction. One-tail Fisher's exact test was conducted on each cross validation and multiple testing was addressed using BH FDR procedure. The results showed that, of the 148 pursued GO biological processes, fifteen terms each had at least one model with FDR-adjusted p-value (Adj.p) <0.05 and six had the values between 0.05 and ...

• Type: Journal
• Year: 2008
• Where: BMCBI
• Authors: Wensheng Zhang, Sige Zou, Jiuzhou Song
• Keywords: Biological Processes, BMCBI 2008, Cross Validation, Valuable Source