a novel method incorporating gene ontology information for unsupervised clustering and feature selection一种新颖的无监督聚类的方法将基因本体信息和特征选择.pdfVIP

A Novel Method Incorporating Gene Ontology Information for Unsupervised Clustering and Feature Selection 1¤ 1 2 1,2,3 Shireesh Srivastava , Linxia Zhang , Rong Jin , Christina Chan * 1 Department of Chemical Engineering and Materials Science, Michigan State University, East Lansing, Michigan, United States of America, 2 Department of Computer Science and Engineering, Michigan State University, East Lansing, Michigan, United States of America, 3 Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, Michigan, United States of America Abstract Background: Among the primary goals of microarray analysis is the identification of genes that could distinguish between different phenotypes (feature selection). Previous studies indicate that incorporating prior information of the genes’ function could help identify physiologically relevant features. However, current methods that incorporate prior functional information do not provide a relative estimate of the effect of different genes on the biological processes of interest. Results: Here, we present a method that integrates gene ontology (GO) information and expression data using Bayesian regression mixture models to perform unsupervised clustering of the samples and identify physiologically relevant discriminating features. As a model application, the method was applied to identify the genes that play a role in the cytotoxic responses of human hepatoblastoma cell line (HepG2) to saturated fatty acid (SFA) and tumor necrosis factor (TNF)-a, as compared to the non-toxic response to the unsaturated FFAs (UFA) and TNF-a. Incorporation of prior knowledge led to a better discriminat