Knowledge-guided multi-scale independent component analysis for biomarker identification

Background: Many statistical methods have been proposed to identify disease biomarkers from gene expression profiles. However, from gene expression profile data alone, statistical methods often fail to identify biologically meaningful biomarkers related to a specific disease under study. In this paper, we develop a novel strategy, namely knowledge-guided multi-scale independent component analysis (ICA), to first infer regulatory signals and then identify biologically relevant biomarkers from microarray data. Results: Since gene expression levels reflect the joint effect of several underlying biological functions, diseasespecific biomarkers may be involved in several distinct biological functions. To identify disease-specific biomarkers that provide unique mechanistic insights, a meta-data "knowledge gene pool" (KGP) is first constructed from multiple data sources to provide important information on the likely functions (such as gene ontology information) and regulatory event...

• Type: Journal
• Year: 2008
• Where: BMCBI
• Authors: Li Chen, Jianhua Xuan, Chen Wang, Ie-Ming Shih, Yue Wang, Zhen Zhang, Eric P. Hoffman, Robert Clarke
• Keywords: BMCBI 2008, Disease-specific Biomarkers, Gene Expression, Microarray Data