A novel disease gene prediction method based on PPI network

To identify the underlying disease gene of human genetic disorders is a challenging and meaningful task in bioinformatics research. Recently, several methods we re developed based on PPI network, motivated by the observation that the disease genes of the same or similar diseases tend to lie close to each other in the PPI network. However, most of these methods based on the direct neighbors or shortest distance between disease genes, which ignore the global information of the PPI network. We develop a novel method for predicting disease gene based on function flow with PPI network. First, we map the known disease genes and candidate disease genes in linkage interval to PPI network. Then, simulate the process of function flow model which propagates information from the known disease genes to other genes (proteins) over the PPI network, and each protein gets a function score. Finally, candidate disease genes are ranked according to function score, the genes (proteins) with high score are considered as disease genes. The experimental results show our method is effective, and it can identify disease genes more accurately.