Prioritizing complex disease risk genes by integrating multiple data

Complex diseases, such as obesity, type II diabetes and chronic obstructive pulmonary disease (COPD) as metabolic disorder-related diseases are major concern for worldwide public health in the 21st century. The identification of these disease risk genes has attracted increasing interest in computational systems biology. In this paper, a novel method was proposed to prioritize disease risk genes (PDRG) by integrating functional annotations, protein interactions and gene expression information to assess similarity between genes in a disease-related metabolic network. The gene prioritization method was successfully carried out for obesity and COPD, the effectiveness of which was superior to those of ToppGene and ToppNet in both literature validation and recall rate by LOOCV. Our method could be applied broadly to other metabolism-related diseases, helping to prioritize novel disease risk genes, and could shed light on diagnosis and effective therapies.