Knowledge-guided gene ranking by coordinative component analysis

Background: In cancer, gene networks and pathways often exhibit dynamic behavior, particularly during the process of carcinogenesis. Thus, it is important to prioritize those genes that are strongly associated with the functionality of a network. Traditional statistical methods are often inept to identify biologically relevant member genes, motivating researchers to incorporate biological knowledge into gene ranking methods. However, current integration strategies are often heuristic and fail to incorporate fully the true interplay between biological knowledge and gene expression data. Results: To improve knowledge-guided gene ranking, we propose a novel method called coordinative component analysis (COCA) in this paper. COCA explicitly captures those genes within a specific biological context that are likely to be expressed in a coordinative manner. Formulated as an optimization problem to maximize the coordinative effort, COCA is designed to first extract the coordinative components...

• Type: Journal
• Year: 2010
• Where: BMCBI
• Authors: Chen Wang, Jianhua Xuan, Huai Li, Yue Wang, Ming Zhan, Eric P. Hoffman, Robert Clarke
• Keywords: Biological Knowledge, BMCBI 2010, COCA, Gene