Detecting disease associated modules and prioritizing active genes based on high throughput data

Background: The accumulation of high-throughput data greatly promotes computational investigation of gene function in the context of complex biological systems. However, a biological function is not simply controlled by an individual gene since genes function in a cooperative manner to achieve biological processes. In the study of human diseases, rather than to discover disease related genes, identifying disease associated pathways and modules becomes an essential problem in the field of systems biology. Results: In this paper, we propose a novel method to detect disease related gene modules or dysfunctional pathways based on global characteristics of interactome coupled with gene expression data. Specifically, we exploit interacting relationships between genes to define a gene's active score function based on the kernel trick, which can represent nonlinear effects of gene cooperativity. Then, modules or pathways are inferred based on the active scores evaluated by the support ve...

• Type: Journal
• Year: 2010
• Where: BMCBI
• Authors: Yu-Qing Qiu, Shi-Hua Zhang, Xiang-Sun Zhang, Luonan Chen
• Keywords: BMCBI 2010, Disease, Dysfunctional Pathways, Gene