Building pathway clusters from Random Forests classification using class votes

Background: Recent years have seen the development of various pathway-based methods for the analysis of microarray gene expression data. These approaches have the potential to bring biological insights into microarray studies. A variety of methods have been proposed to construct networks using gene expression data. Because individual pathways do not act in isolation, it is important to understand how different pathways coordinate to perform cellular functions. However, there are no published methods describing how to build pathway clusters that are closely related to traits of interest. Results: We propose to build pathway clusters from pathway-based classification methods. The proposed methods allow researchers to identify clusters of pathways sharing similar functions. These pathways may or may not share genes. As an illustration, our approach is applied to three human breast cancer microarray data sets. We found that our methods yielded consistent and interpretable results for thes...

• Type: Journal
• Year: 2008
• Where: BMCBI
• Authors: Herbert Pang, Hongyu Zhao
• Keywords: BMCBI 2008, Gene Expression Data, Informative Genes, Pathway Clusters